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CHAPTER ONE
1.1. Background
Diabetes mellitus is the commonest endocrine-metabolic disorder characterized by
chronic hyperglycaemia giving rise to the risk of microvascular (retinopathy,
nephropathy, and neuropathy) and macrovascular (ischaemic heart disease, stroke and
peripheral vascular disease) damage, with associated reduced life expectancy and
diminished quality of life Diabetes mellitus may present with characteristic symptoms
such as thirst, polyuria, blurring of vision, and weight loss !n its most severe forms,
ketoacidosis or a non"ketotic hyperosmolar state may develop and lead to stupor, coma
and, in absence of effective treatment, death (#$%, &''') eople with diabetes are at
increased risk of cardiovascular, peripheral vascular and cerebrovascular disease
everal pathogenetic processes are involved in the development of diabetes *hese
include processes, which destroy the beta cells of the pancreas with consequent insulin
deficiency, and others that result in resistance to insulin action *he abnormalities of
carbohydrate, fat and protein metabolism are due to deficient action of insulin on target
tissues resulting from insensitivity or lack of insulin (#$%, &''') *he prevalence of
diabetes is increasing rapidly worldwide and the #orld $ealth %rganization (+) has
predicted that by + the number of adults with diabetes would have almost doubled
worldwide, from &.. million in + to . million /xperts pro0ect that the incidence
of diabetes is set to soar by 123 by ++45 meaning that the disease will affect a
staggering 4& million citizens (6owley and 7ezold, +&+) *he estimated worldwide
prevalence of diabetes among adults in +& was +84 million (123) and this value is
predicted to rise to around 2' million (..3) by + (haw et al, +&) 6ecent
estimates indicate there were &.& million people in the world with diabetes in the year
+ and this is pro0ected to increase to 11 million by + *his increase in
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prevalence is expected to be more in the 9iddle /astern crescent, ub-aharan :frica
and !ndia !n :frica, the estimated prevalence of diabetes is &3 in rural areas, up to .3
in urban sub-ahara :frica, and between 8-&3 in more developed areas such as outh
:frica and in population of !ndian origin :frica (onny ; et al., 2011). *he
prevalence in
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also influence the risk of developing type + diabetes ;onsumption of sugar-sweetened
drinks in excess is associated with an increased risk (9alik C et al., 2010) *he type
of fatsin the diet is also important, with saturated fatsand trans " fatty increasing the
risk andpolyunsaturated and monounsaturated fatdecreasing the risk /ating lots of
white riceappears to also play a role in increasing risk ($u /: et al., 2012).
*he Diabetes is diseases that has significant burden on and healthcare systems
or this study, researchers had developed
software called Diab-memory to support patients entering their information such as
blood-glucose level, in0ected insulin doses, food intake, well-being and physical
activities *hen, data were remotely synchronized to a central database *he system was
based on ava+ 9obile edition (+9/) and built using state of the art internet
technology
*he study sample was & patients with *&D9 9ean age was 11 years (E&& years)
being in the trail study for three months *he result was focused on patientsF adherence
to the therapy, availability of the monitoring system and the effects on metabolic status
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:s questionnaire shows, the system was accepted in general, and this shows that the
role of information system in the health sector cannot be overlooked
*owards reducing the burden of D9 (ma0orly *+D9) in
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the development of the nations by depriving them of valuable human resources in their
most productive years (#$%, ++) *his is because Diabetes mellitus diseases could
eventually lead to disabilities such as stroke and thus, scarce family and societal
resources are directed to the costly and prolonged medical care of such ones (#$%,
++) *herefore, the challenge of this pro0ect is to understand the risk factors or
variables that are responsible for the likelihood of *ype + Diabetes 9ellitus (*+D9)
disease occurrence and evaluate the likelihood of *+D9 disease based on these
variables
1. Sco!e of the Problem
*his pro0ect is limited in scope by the development of a predictive model for *ype +
Diabetes 9ellitus risk using >uzzy =ogic model
1." A#m and Ob$ect#%e& of the &tud'
*he aim of this study is to develop a model for prediction of *+D9 disease using the
>uzzy =ogic 9odel
*he specific ob0ective of this study is toG
(i) identify variables required for predicting *+D9 disease risk(ii) simulate the model(iii) validate the model
1.( )ethodolog'
!n order to achieve the aforementioned ob0ectives, the methodology approach will be as
followsG
(i) /xtensive review of related work on diabetes mellitus prediction will be
done followed by formal interview with disease expert (/ndocrinologists) to
elicit knowledge on variables relevant for disease risk identification(ii) *he fuzzy logic will be used to develop the predictive model for type +
diabetes mellitus disease risk using the variables identified in (i)(iii) imulate the *ype + Diabetes 9ellitus diseases risk predicting system using
the model in (ii)
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(iv) *he performance of the model will be evaluated using performance metrics
likeG accuracy, sensitivity, precision and recall (&-specificity)
1.* +u&t#f#cat#on of the Stud'
*his study is necessitated by the need to prevent calamitous outbreak of diseases that
may send many to untimely grave with the aim of an early detection system (#$%,
++), is pitched towards prevention, and planned response to this terminal disease *o
gain a better knowledge of disease incidence and risk factors so as to control them@ with
the aim of improving the health care delivery in
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variables and aggregation of the output variables, the software used for the
implementation of the system ;hapter four gives detailed information about the system
design, implementation and the tools used in the development of the system !t also
gives a description of the user interface, which the user uses in interacting with the
system >inally, chapter five concludes the work by stating the summary, conclusion
and recommendation of the work done
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8
CHAPTER T-O
/TERAT0RE RE/E-
2.1 /ntroduct#on
:ccording to Detmer (&''.), epidemiology is the study of the distribution and
patterns of health-events, health-characteristics and their causes or influences in well-
defined populations !t is the cornerstone method of public health research and
practice, and helps inform policy decisions and evidence-based medicine by
identifying risk factors for diseases and targets for preventive medicine and public
policies /pidemiologists are involved in the design of studies, collection and
statistical analysis of data, and interpretation and dissemination of results (including
peer review and occasional systematic review) %ver the past years, epidemiology
has significantly contributed to improve methods used in clinical research and, to a
lesser extent, basic (microbiological, genetic) research (ankowski, &''') 9a0or
areas of epidemiological study include bio monitoring, and comparisons of treatment
effects such as in clinical trials, outbreak investigation, diseases surveillance and
screening (medicine) /pidemiologists rely on a number of other scientific disciplines
such as 7iology (to better understand diseases processes), 7iostatistics (to make
efficient use of the data and draw appropriate conclusions), and /xposure assessment
and ocial science disciplines (to better understand proximate and distal risk factors,
and their measurement (7ourlas et al, &''')
*he advancement in computer technology has encouraged the researchers to develop
software for assisting doctors in making decision without consulting the specialists
directly *he software development exploits the potential of human intelligence such
as reasoning, making decision, learning (by experiencing) and many others :rtificial
intelligence is not a new concept, yet it has been accepted as a new technology in
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computer science !t has been applied in many areas such as education, business,
medical and manufacturing *his pro0ect explores the potential of artificial
intelligence techniques in determining the likelihood of Diabetes mellitus diseases in
an individual given a number of associated risk factors
2.2 #abete& )ell#tu& #&ea&e&
*ype + diabetes mellitus (*+D9) is the commonest form of diabetes affecting more
than '3 of the diabetic population worldwide *here is a rapid upsurge in the
number of diabetic patients and this explosive growth is noted in both urban and rural
areas #ild et al estimated the number of *+D9 patients in the year + at &.2
million and predicted it to increase to 11 million in + Diabetes mellitus (D9) is
a serious condition with potentially devastating complications that affects all age
groups worldwide !n &'84, an estimated million people around the world were
diagnosed with diabetes@ in +, that figure rose to over &4 million@ and, in +&+,
the !nternational Diabetes >ederation (!D>) estimated that .& million people had
diabetes *hat number is pro0ected to rise to 44+ million (or & in & adults) by +,
which equates to three new cases per second (onny ; et al., 2011). *his increase in
prevalence is expected to be more in the 9iddle /astern crescent, ub-aharan :frica
and !ndia !n :frica, the estimated prevalence of diabetes is &3 in rural areas, up to
.3 in urban sub-ahara :frica, and between 8-&3 in more developed areas such as
outh :frica and in population of !ndian origin *he prevalence in
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2.2.1 E!#dem#olog' of T'!e 2 #abete& )ell#tu& #&ea&e&
9ortality rates generally appear to be most closely linked to a country?s stage of
epidemiological transition /pidemiological transition, a concept first proposed by
:bdel %mran in the &'.s (%mran, &'.&), refers to the changes in the predominant
forms of diseases and mortality burdening a population that occur as its economy and
health systems develops !n underdeveloped countries at the early stages of
epidemiological transition, infectious diseases predominate, but as the economy,
development status, and health systems of these countries improve, the population
moves to a later stage of epidemiological transition, and chronic non-communicable
diseases become the predominant causes of death and diseases (Haziano et al, +1)
6ecent estimates indicate there were &.& million people in the world with diabetes in
the year + and this is pro0ected to increase to 11 million by + Diabetes is a
condition primarily defined by the level of hyperglycaemia giving rise to risk of
microvascular damage (retinopathy, nephropathy and neuropathy) !t is associated
with reduced life expectancy, significant morbidity due to specific diabetes related
microvascular complications, increased risk of microvascular complications
(ischaemic heart disease, stroke and peripheral vascular disease), and diminished
quality of life *he :merican Diabetes :ssociation (:D:) estimated the national
costs of diabetes in the I: for ++ to be Jus&+ billion, increasing to Jus&'+
billion in ++ (#$%, +1)
2.2.2 Aet#olog' Of #abete& )ell#tu&3 Non4/n&ul#n e!endenc' #abete&
)ell#tu& 5N/)6
diabetes mellitus, which is the predominant form of diabetes and accounts for at least
'3 of all cases of diabetes mellitus (Honzalez et al, +') *he rise in prevalence
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is predicted to be much greater in developing than in developed countries (1'
versus +3) (haw et al, +&)
!n developing countries, people aged 2 to 1 years (that is, working age) are affected
most, compared with those older than 1 years in developed countries (haw et al,
+&) *his increase in type + diabetes is inextricably linked to changes towards a
#estern lifestyle (high diet with reduced physical activity) in developing countries
and the rise in prevalence of overweight and obesity (;han et al, +'@ ;olagiuri,
+&) *here are approximately &2 million people with diagnosed type + diabetes in
the IA (7ennett et al, &''4) *he incidence of diabetes increases with age, with most
cases being diagnosed after the age of 2 years *his equates to a lifetime risk of
developing diabetes of & in & (
6isk factors can be either modifiable or non-modifiable 9odifiable risk factors
include@ smoking, obesity, sedentary lifestyle, and lipid disorders
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*he effects of risk factors is multiplicative rather than additive, thus people with a
combination of risk factors (for example, smoking, obesity and hypertension) have the
greatest risk of developing heart diseases !t is important to distinguish between
relative risk (the proportional increase in risk) and absolute risk (the actual chance of
an event) *hus, a 4 year old man with a plasma cholesterol of &mmolKlitre who
smokes 2 cigarettes a day is relatively much more likely to die from coronary
diseases within the next decade than a non-smoking woman of the same age with a
normal cholesterol, but the absolute likelihood of his dying during this time is still
small (high relative risk, low absolute risk) (7lessey, &'84)
roximal risks for *+D9 include those associated with consumption patterns (mainly
linked to diets, tobacco and alcohol use), activity patterns, and health service use as
well as biological risk factors such as increased cholesterol, blood pressure, blood
glucose, and clinical diseases *he >ramingham tudy first centred attention on the
concept of Lrisk factorsM associated with *+D9, and most recently reported
substantial -years risk data showing the accumulation of risk over time (encina et
al, +') !mportantly, risk factors for the incidence of *+D9 and those associated
with *+D9 severity or mortality are not synonymous 6isk factors for incidence
become important starting very early in life and accumulate with behavioural, social,
and economic factors over the life course to culminate in biological risks for *+D9
such as increased blood pressure, blood glucose, and clinical diseases %ver the past
few decades, the effectiveness of early screening and long-term treatment for
biological risks or early diseases has contributed to the sharp declines in D9
mortality seen in many countries ($umink et al, &''.) *he :merican Diabetes
:ssociation Huide to Diabetes 9edical
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mellitus asG age N 24 years, ethnicity, family history, habitual physical inactivity,
overweight (79! N +4 kgKm+), hypertension (N !2K' mm $g in adults), and
previously diagnosed impaired fasting glucose or impaired glucose tolerance, $D=
cholesterol O 4mgKdl) andKor triglyceride level (P+4 mgKdl), polycystic ovary
syndrome, and history of vascular disease
*he recent #$% Hlobal $ealth 6isks 6eport of +' (=opez et al, +1) and the
earlier #orld $ealth 6eport of ++ provide comparable and robust estimates of the
contribution of risks to total mortality and measures of disability (9athers et al, +@
#$%, ++, +'b) 6elatively few ma0or behavioural and biological risk factors
account for *+D9 incidence around the world *obacco use, diet (including alcohol,
total calorie intake, and specific nutrients) and physical inactivity serve as the three
ma0or behavioural risks 7etween them, they account for a significant proportion of
cancer, cardiovascular disease, and chronic respiratory diseases incidence in addition
to D9 ($u et al, +&@ #$%, ++@ Bach et al, +2, +4@ Can Dam et al, +8)
;oncerted action focused on these behavioural risks, along with biological risks such
as high blood pressure, high blood lipids, and high blood glucose, would have a wide
impact on the global incidence and burden of diseases (#$%, +'b) $igh blood
pressure, tobacco use, elevated blood glucose, physical inactivity, and overweight and
obesity are the five leading factors globally !n middle income countries, alcohol
replaces high blood glucose in the top five@ in low income countries, a lack of safe
water, unsafe sex, and under " nutrition are important *hese latter points are related
to both the role of early childhood nutrition in the later onset of cardiovascular disease
and D9 as well as the need to integrate the management of $!CK:!D more closely
with D9 in low-income countries (#$%, +'b)
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2..1 Bod' )a&& /nde83 O%er9e#ght and Obet'
%verweight and obesity are defined as abnormal or excessive fat accumulation that
presents a risk to health : crude population measure of obesity is the body mass
index (79!), a person?s weight (in kilograms) divided by the square of his or her
height (in metres) : person with a 79! of or more is generally considered obese
: person with a 79! equal to or more than +4 is considered overweight (#$%,
+&4) %verweight and obesity are ma0or risk factors for a number of chronic
diseases, including diabetes, cardiovascular diseases and cancer %nce considered a
problem only in high-income countries, overweight and obesity are now dramatically
on the rise in low- and middle-income countries, particularly in urban settings (#$%,
+&4) :ccording to =ebovitz (+2), overweight and obesity is a risk factor for
developing type + diabetes *he best measure of overweight and obesity is the body
mass index (79!) %verweight status, a 79! of equal to or greater than +4 kgKm+,
and obesity, a 79! greater than or equal to kgKm+, have become a problem
throughout the #orld 79! levels of this proportion cause an increased risk of
developing many types of chronic diseases, including type + diabetes mellitus !n fact,
the term QdiabesityQ has been used to demonstrate the close link between type +
diabetes mellitus and obesity
2..2 Smok#ng
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#annamethee, haper, and erry (+&) concluded that smoking is considered a risk
factor for developing type + diabetes mellitus
2.. Age
:ge is an important risk factor in developing cardiovascular and diabetes mellitus
diseases, it is estimated that 8. percent of people who die of coronary heart diseases
are 1 and older (:merican $eart :ssociation, +&) !n the age group of +-22 years,
it was estimated about .3 people had diabetes mostly the type + diabetes mellitus@
while in the age group 24-12 years the number increased to &.3@ and the highest
percentage of +1'3 was found in the age group of N14 years (;entres for Disease
;ontrol and revention, +&&) imilar feature was also observed in /ngland, where
the prevalence of diabetes was increasing with age *he peak prevalence of type +
diabetes can be found in the age group of 14-.2 years with &4.3 in men and &23
in women (helton, +1) *+D9 becomes increasingly common with advancing age
" :s a person gets older@ the body undergoes subtle physiologic changes, even in the
absence of diseases (#$%, +8b)
2.." Ph'cal Act#%#t'
#$% and >:% highlighted the importance of physical activity as a key determinant
of obesity, ;CD, and diabetes (oint #$%K>:% /xpert ;onsultation, +)
hysical activity is defined as any bodily movement produced by skeletal muscles that
require energy expenditure !t has been identified as the fourth leading risk factor for
global mortality causing an estimated + million deaths globally hysical activity is
a key determinant of energy expenditure, and thus is fundamental to energy
balance and weight control, hysical activity reduces risk for cardiovascular
diseases and diabetes and has substantial benefits for many conditions, not only
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those associated with obesity *he beneficial effects of physical activity on the
metabolic syndrome are mediated by mechanisms beyond controlling excess body
weight >or example, physical activity reduces blood pressure, improves the level of
high-density lipoprotein cholesterol, improves control of blood glucose in overweight
people, even without significant weight loss, and reduces the risk for colon cancer and
breast cancer among women (#$%, +2)
2..( 0rban4Rural #fference&
6esidence seems to be a ma0or determinant of type + diabetes in ub-aharan :frica
ince urban residents have &4- to 2 times higher prevalence of type + diabetes than
their rural counterparts *his is attributable to lifestyle changes associated with
urbanization and #esternization Irban lifestyle in :frica is characterized by changes
in dietary habits involving an increase in the consumption of refined sugars and
saturated tut and a reduction in liber intake (9ennen et al +) ohngwi and
colleagues (++) have recently reported an increase in fasting plasma glucose in
those whose lives have been spent in an urban environment, suggesting that both
lifetime exposure to and recent migration to or current residence in an urban
environment are potential risk factors for obesity and type + diabetes mellitus *he
disease might represent the cumulative effects over years of dietary changes, decrease
in physical activity, and psychological stress
*he population of :frica is predominantly rural, but the &''4R+ urban growth
rate was estimated at 2 percent (compared with 4 percent in /urope) *hus, more
than . percent of the population of :frica will he urban residents by ++4 (I:
+) *here will therefore be a tremendous increase in the prevalence of type +
diabetes attributable to rapid urbanization
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2..* :ender
!n the first half of the last century, the prevalence of type + diabetes was higher among
women than among men, but this trend has shifted, so more men than women are now
diagnosed with type + diabetes *his change in the gender distribution of type +
diabetes is mainly caused by a more sedentary lifestyle particularly among men,
resulting in increased obesity $owever, recent data have also shown that men develop
type + diabetes at a lower degree of obesity than women " a finding that adds support
to the view that the pathogenesis of type + diabetes differs between men and women
%bservations of sex differences in body fat distribution, insulin resistance, sex
hormones, and blood glucose levels further support this notion (>Srch, A, +&2) *he
body fat distribution, especially the abdominal visceral fat is associated with increased
type + diabetes risk 7ody fat distribution differs by sex (=ogue et al, +&&), and in
general men have more abdominal fat, whereas women have more peripheral fat "
also denoted as LappleM versus LpearM shape =ooking into the abdominal fat, men
also tend to have more visceral and hepatic fat than women do, whereas women have
more subcutaneous fat than men do !n contrast to visceral fat, subcutaneous fat is
associated with improved insulin sensitivity and is therefore protective against type +
diabetes *hus, the phenomenon that men develop diabetes at a lower body mass
index than women can be explained by the fact that men have more visceral fat for a
given body mass index than women and thereby a higher relative risk for developing
type + diabetes (=ogue et al, +&&)
2.., 7am#l' H#&tor' of #abete&
*here is also ample evidence that type + diabetes has a strong genetic basis *he
concordance of type + diabetes in monozygotic twins is T.3 compared with +"
3 in dizygotic twins (Caleriya = et al, +&) *he lifetime risk of developing the
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disease is T23 in offspring of one parent with type + diabetes, greater if the mother
is affected and approaching .3 if both parents have type + diabetes !n prospective
studies, we have demonstrated that first-degree family history is associated with
twofold increased risk of future type + diabetes (Caleriya = et al, +&) *he
challenge has been to find genetic markers that explain the excess risk associated with
family history of type + diabetes : significant proportion of the offspring of
;ameroonians with type + diabetes have either type + diabetes (2 percent) or !H* (8
percent) (9hanya et al +) : positive family history seems to be an independent
risk factor for type + diabetes, but this was not the case in the ;ape *own study
(=evitt et al, &''), in which family history has not an independent risk factor
2..; Pred#abete&
!n &''. and +, the /xpert ;ommittee on Diagnosis and ;lassification of Diabetes
9ellitus (/xpert ;ommittee on the Diagnosis and ;lassification of Diabetes 9ellitus,
&''., Henuth , et al., 2003) recognized an intermediate group of individuals whose
glucose levels do not meet criteria for diabetes, yet are higher than those considered
normal *hese people were defined as having impaired fasting glucose (!>H) Ufasting
plasma glucose (>H) levels & mgKdl (41 mmolKl) to &+4 mgKdl (1' mmolKl)V, or
impaired glucose tolerance (!H*) U+-h values in the oral glucose tolerance test
(%H**) of &2 mgKdl (.8 mmolKl) to &'' mgKdl (&& mmolKl)V !ndividuals with !>H
andKor !H* have been referred to as having prediabetes, indicating the relatively high
risk for the future development of type + diabetes !>H and !H* should not be viewed
as clinical entities in their own right but rather risk factors for type + diabetes as well
as cardiovascular disease (:D:, +&2)
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2." #agno& of T'!e 2 #abete& )ell#tu& #&ea&e&
!f a diagnosis of diabetes is made, the clinician must feel confident that the diagnosis
is fully established since the consequences for the individual are considerable and
lifelong *he requirements for diagnostic confirmation for a person presenting with
severe symptoms and gross hyperglycaemia differ from those for the asymptomatic
person with blood glucose values found to be 0ust above the diagnostic cut"off value
evere hyperglycaemia detected under conditions of acute infective, traumatic,
circulatory or other stress may be transitory and should not in itself be regarded as
diagnostic of diabetes *he diagnosis of type + diabetes in an asymptomatic sub0ect
should neverbe made based on a single abnormal blood glucose value >or the
asymptomatic person, at least one additional plasmaKblood glucose test result with a
value in the diabetic range is essential, either fasting, from a random (casual) sample,
or from the oral glucose tolerance test (O:TT) !f such samples fail to confirm the
diagnosis of diabetes mellitus, it will usually be advisable to maintain surveillance
with periodic re"testing until the diagnostic situation becomes clear !n these
circumstances, the clinician should take into consideration such additional factors as
ethnicity, family history, age, adiposity, and concomitant disorders, before deciding on
a diagnostic or therapeutic course of action :n alternative to blood glucose
estimation or the %H** has long been sought to simplify the diagnosis of diabetes
Hlycated haemoglobin, reflecting average glycaemia over a period of weeks, was
thought to provide such a test :lthough in certain cases it gives equal or almost equal
sensitivity and specificity to glucose measurement (9c;ance D 6, &''2), it is not
available in many parts of the world and is not well enough standardized for its use to
be recommended at this time
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2.".1 S!ec#f#c Te&t& for T'!e 2 #abete& )ell#tu& S'&tem
*esting enables health care providers to find and treat diabetes before complications
occur and to find and treat prediabetes, which can delay or prevent type + diabetes
from developing :lthough not all tests are recommended for diagnosing all types of
diabetes, but the any one of the following tests can be used for diagnosisG
:&; *est, also called the haemoglobin :&c, $b:&c, or glycol haemoglobin
test
>asting lasma Hlucose (>H) *est
%ral Hlucose *olerance *est (%H**)
6andom lasma Hlucose (6H) *est2.".1.1 A1C Te&t
*he :&; test is used to detect type + diabetes and prediabetes but is not recommended
for diagnosis of type & diabetes or gestational diabetes *he :&; test is a blood test
that reflects the average of a person?s blood glucose levels over the past months and
does not show daily fluctuations *he :&; test is more convenient for patients than
the traditional glucose tests because it does not require fasting and can be performed
at any time of the day *he :&; test result is reported as a percentage *he higher the
percentage, the higher a person?s blood glucose levels have been L: normal :&;
level is below 4.3, and :&; of 4. to 12 3, indicates prediabetes eople
diagnosed with prediabetes may be retested in & year eople with an :&; below 4.
percent may still be at risk for diabetes, depending on the presence of other
characteristics that put them at risk, also known as risk factors eople with an :&;
above 13, should be considered at very high risk of developing diabetes : level of
14 percent or above means a person has diabetesM (
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*he >asting lasma Hlucose (>H) test is used to detect type + diabetes and
prediabetes *he >H test has been the most common test used for diagnosing
diabetes because it is more convenient than the %H** and less expensive (H test measures blood glucose in a person who has fasted for at least 8
hours and is most reliable when given in the morning eople with a fasting glucose
level of & to &+4 mgKdl have impaired fasting glucose (!>H), or prediabetes : level
of &+1 mgKdl or above, confirmed by repeating the test on another day, means a
person has diabetes
2.".1. Oral :luco&e Tolerance Te&t
:ccording to
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*he most common signs and symptoms of diabetes areG
>requent urination
Disproportionate thirst
!ntense hunger
#eight gain
Inusual weight loss
!ncreased fatigue
!rritability
7lurred vision
;uts and bruises donFt heal properly or quickly
9ore skin andKor yeast infections
!tchy skin
Hums are red andKor swollen
>requent gum diseaseKinfection
exual dysfunction (men)
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researchers have employed D* to resolve various biological problems, including
diagnostic error analysis (9urphy, +&), potential biomarker finding (Wu et al, ++@
#on et al, +), and proteomic mass spectra classification (Heurts et al, +4)
7ayesian networks are a probability-based inference model, increasingly used in the
medical domain as a method of knowledge representation for reasoning under
uncertainty for a wide range of applications, including diseases diagnosis (7alla et al,
&'84), genetic counselling ($arris, &''), expert system development (tockwell,
&''), gene network modelling (=iu et al, +1), and emergency medical decision
support system (9D) design (adeghi et al, +1)
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al, +1) *he three-layered 9= with 2 categorical input variables and modified
learning method achieved a diagnosis accuracy of over '3
upport vector machines are a new and promising classification and regression
technique proposed by Capnik and his co-workers (;ortes X Capnik, &''4@ Capnik,
&''4) C9s, developed in statistical learning theory, are recently of increasing
interest to biomedical researchers *hey are not only theoretically well-founded, but
are also superior in practical applications >or medical, clinical decision support and
biological domains, C9s have been successfully applied to a wide variety of
application domains, including 9D for the diagnosis of tuberculosis infection
(Ceropoulos, et al, &'''), tumour classification (chubert, et al, +), myocardial
infarction detection (;onforti X Huido, +4), biomarker discovery (rados et al,
+2), and cancer diagnosis (9a0umder, et al, +4)
*o overcome the limited generalization performance of single models and simple
model combination approaches, more precise model combination methods, called
YYen&emble method&
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Diabetes is known as one of the most common diseases that has significant
burden on patients and healthcare systems
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input data (ie independent variables) that have a measurable causal or coincident
relationship to the output !n order for predictive modelling to be useful in a given
application, two fundamental principles must holdG
i%utcomes must have some level of predictability from known data *hat is, similar
patterns represented across model inputs should be indicative of similar outputs@
ii *here exist some measurable relationship between the set of known data values
that will be used as model inputs and the resulting output value(s) that the
model is tasked to approximate@ and
iii 6elationships that existed in the past will continue to hold in the future such that
it is reasonable to use past observations to infer future behaviour
#hen these principles are adhered to, predictive modelling can approximate the
relationship between the known input data measures and the resulting output
2.(. Pred#ct#%e modell#ng a!!l#cat#on&
*here are generally two classes of predictive modelling applications that differ by the
type of output the model producesG
#. 7oreca&t#ng3>orecasting model generate outputs that are continuous-valued *hat is,
the output should be a value ranging from the minimum to the maximum
allowed *hese models are used in applications such as forecastingKestimatingG
sales, volumes, costs, yields, rates, temperatures, scores, etc and
##. Cla&f#cat#on3 ;lassification models generate outputs that are &-of-n discrete
possible outcomes %ften there is a single output that represents a 7oolean (ie,
yesKno) outcome *hese models are used in pattern recognition applications to
do fraud detection, target recognition, vote forecasting, prospect classification,
churn prediction, bankruptcy prediction, etc *his is the preferred methodology
for the implementation of the predictive model for the intended system
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2.* Related -ork&
: few number of prediction systems exists concerning Diabetes 9ellitus and other
related diseases such as ;ardiovascular diseases prediction with varying factors and
data mining methodology applied
2.*.1 A )ach#ne earn#ng A!!roach to Pred#ct#ng Blood :luco&e e%el& for
#abete& )anagement
*his system, Aevin et al (+&2), describe a solution that uses a generic
physiological model of blood glucose dynamics to generate informative features for a
upport Cector 6egression model that is trained on patient specific data *he new
model outperforms diabetes experts at predicting blood glucose levels and could be
used to anticipate almost a quarter of hypoglycaemic events minutes in advance
:lthough the corresponding precision is currently 0ust 2+3, most false alarms are in
near-hypoglycaemic regions and therefore patients responding to these
hypoglycaemia alerts would not be harmed by intervention (Aevin et al., +&2)
2.*.2 /ntell#gent Heart #&ea&e& Pred#ct#on S'&tem 5/HPS6 ung -e#ghted
A&&oc#at#%e Cla&f#er&
yoti et al (+&&) designed the !$D system as a HI! based !nterface to enter the
patient record and predict whether the patient is having $eart diseases or not using
#eighted :ssociation rule based ;lassifier *he prediction is performed from mining
the patient?s historical data or data repository !n #eighted :ssociative ;lassifier
(#:;), different weights are assigned to different attributes according to their
predicting capability *he system has been implemented in 0ava latform and trained
using benchmark data from I;! machine learning repository *he system is
expandable for the new dataset
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*he system is a #eb-based, user-friendly, scalable and reliable that can be
implemented in remote areas like rural regions or countryside, to imitate like human
diagnostic expertise for treatment of heart ailment *he system is expandable in the
sense that more number of records or attributed can be incorporated and new
significant rules can be generated using underlying Data 9ining technique resently
the system has been using & attributes and records only and the data is from I;!
machine learning dataset that is mainly used for research purpose :s the symptoms
that cause a particular disease may vary from region to region, the system should be
trained using local dataset collected from the clinic
2.*. ec#on Su!!ort #n Heart #&ea&e& Pred#ct#on S'&tem 5SHPS6 ung
Na=%e Ba'e&
*he D$D was developed by ubbalakshmi et al (+&&) using or, example it can
incorporate other medical attributes besides the one used !t can also incorporate other
data mining techniques ;ontinuous data can be used instead of 0ust categorical data
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Table 2.1 Table of related 9ork& to #abete& )ell#tu& Pred#ct#on
S>N Author5&6 Re&earch T#tle Sco!e Strength& #m#tat#on& Remark&
& Aevin ,
6azvan 7,
;indy 9, ay
, and >rank
: (+&2)
: 9achine
=earning
:pproach to
redicting 7lood
Hlucose =evels
for Diabetes
9anagement
7lood
Hlucose
=evels
*he system incorporate
upport Cector
6egression (C6) model,
informed by a
physiological model and
trained on patient specific
data, has outperformed
diabetes experts at
predicting blood glucose
levels and can predict
+3 of hypoglycaemic
events minutes in
advance
*he C6 system was
able to predict +3 of
the hypoglycaemic
events with a false
positive rate under &3
*he system performs
prediction using blood
glucose datasets collected
from *ype & D9
patient?s and C6
model with hysiological
features
+ yoti oni,
Izma :nsari,
Dipesh
harma,
unita oni(+&&)
!ntelligent and
/ffective $eart
Disease
rediction
ystem using#eighted
:ssociative
;lassifiers
$eart
Disease
redictio
n
*he system incorporates
patient health record with
a detailed genetic
analysis *here is a need
to combine these factorsto provide a better overall
determinant of risk
*he prediction is
performed from mining
the patient?s historical
data, which is from
I;! machine learningdataset, which is
mainly used for
research purpose
*he system performs
prediction using patient?s
health history and
-e#ghted :ssociation
rule based ;lassifier
Hubbalaksh
mi, 9*ech,
A 6amesh,
9*ech, 9
;hinna 6ao,
hD (+&&)
Decision
upport in $eart
Disease
rediction
ystem using
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disease !t is implemented
as web based
questionnaire application
and can serve as a training
tool to train nurses and
medical students to
diagnose patients
with respect to ease of
model interpretation,
access to detailed
information and
accuracy
2 runa,
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CHAPTER THREE
RESEARCH )ETHOOO:?
.1 /ntroduct#on
*he research methodology focused on the identification of the different variables required
for predicting the risk of *+D9 in patients from pecialist in the ;ollege of 9edicine,
%bafemi :wolowo Iniversity, !le " !fe via the use of structured interview followed by the
formulation of the fuzzy logic " based model for predicting the risk of *+D9 in such
patients through the use of 9:*=:7 fuzzy logic toolbox
.2 ar#able& e&cr#!t#on
!n this study, the work is limited to six paramount risk factors of the *+D9 only since the
work is intended to provide a system, which aids preventive medicine via the earlier
detection of the disease risk *he causatives variables of *+D9 were classified according to
the groups that they belong to and may only be used to identify the status of the individual
risk to these groups (see *able &)
*he risk factors of those set of variables that help in the identification of the risk of *+D9
includeG
i 7ody 9ass !ndex (79!)G this is a measure of the ratio of the height (in meters) to
the square of the weight (in Ag) used in identifying the likelihood of obesity *he
risk of diabetes and cardiovascular disease increases and the body mass index
increasesii :geG this is another ma0or determinant of the *ype + Diabetes 9ellitus disease
because the higher the age (from years old) the higher the likelihood of the
*+D9 diseaseiii >amily $istory of DiabetesG *his is another identification of the existence of
family members who have had *+D9 or are still living with the disease *he risk
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of *+D9 increases with the existence of family members especially the first
generation membersiv 7lood ressureG this is the measure of systolic and diastolic blood pressure of the
individual and has a benchmark *he risk of *+D9 increases with the increase in
blood pressurev $istory of Hestational DiabetesG Hestational diabetes is the type of diabetes that
usually affects the women during pregnancy *he risk of *+D9 increases with
patient that has had occurrences of Hestational Diabetesvi HenderG *he recent data have also shown that men develop type + diabetes at a
lower degree of obesity than women " a finding that adds support to the view that
the pathogenesis of type + diabetes differs between men and women
%bservations of sex differences in body fat distribution, insulin resistance, sex
hormones, and blood glucose levels further support this notion (>Srch, A, +&2)
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Table .1 R#&k 7actor& A&&oc#ated 9#th T2)
S>N R#&k 7actor& abel&
& >amily $istory of *+D9
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. The mathemat#cal model u&ed for T2) !red#ct#on
*he front-end via which the user will be communicating with the system requires certain
rules which consists of a combination of values of labels of each risk factors required by the
system in determining the status of the patients? *ype + diabetes mellitus status *he >uzzy
=ogic model used in developing the *+D9 prediction system is a qualitative computational
approach, which describes uncertainty or partial truth >uzzy logic has ranging value of
and & that corresponds to the Ldegree of truthM /very set that does not reflect a crisp set, but
has clearly defined boundary is a fuzzy set >uzzy sets represents simple linguistic concepts
like yes-no, true-false, low-medium-high, etc : given element may belong to more than one
fuzzy set at the same time, because the theory of fuzzy sets us a theory of graded concepts
and membership elasticity (!dowu :, et al, 2015) :ll fuzzy sets are characterized by
membership functions " Ya curve that defines how each point in the input space is mapped to
a membership value or degree of membership between and & *he input space is
sometimes referred to as the universe of discourse? (9athwork, +&&)
>or the purpose of this study, there is need to make a general description of the mathematical
model of the proposed fuzzy logic model that was used *he mathematical model of the
fuzzy logic was used to generate the membership functions that were used to map the label
of each variable to their respective fuzzified value using a process called fuzzification *he
membership function that was used in this study in fuzzifying the variables (input and
output) is the triangular membership function in equation &@this function maps the label of
each variable using a triangular " shaped function which uses three () points to define the
two (+) base points and one (&) apex point *he apex point is usually defined by using a
parameter between the two base points
*he mathematical representation of the triangular membership function used to map the
labels of each variables (input and output) is as followsG
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f(x ; a , b , c)=
{
0,x axaba
,a x b
cxcb
, b x c
0,c x
(3.1)
or
f(x ; a , b , c )=max(min(xaba,cxcb ) ,0) (3.2)
#here a and c are the base of the triangle andb is the apex point of the triangle, andx is the
label value within the interval a and c
!n this study, all variables were divided into three labels each represented by its own
triangular membership functions defining their respective base points and apex points *hus,
in the process of fuzzification all variables were mapped using the following membership
functions as defined below, for the three () labels of each variable and that of two (+) labels
of each variable as follow in equations , 2, 4, 1, and . respectivelyG
>or the variables with two labels, the membership functions will beG
label1= f(x ;0.00, 0.25,0.5 ) .(3.3)
label2=f(x ;0.50,0.75,1.00 ) ..(3.4)
#hile for the variables with three labels, the membership functions will beG
label1= f(x ;0.00, 0.16,0.33 ) ..(3.5)
label2=f(x ;0.33,0.49,0.66 ) .(3.6)
label3= f(x ;0.66, 0.83,1.00 ) .(3.7)
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." >u@@' og#c )odel for Pred#ct#ng T'!e 2 #abete& )ell#tu& #&ea&e R#&k
*he fuzzy logic model for predicting the risk of *+D9 involves the process of fuzzification
" defining the input and output variables in the >uzzy !nference ystem (>!), construction
of rule-based for the inference engine, the aggregation of the rules and then the
defuzzification of the results of the aggregated membership function
*he first process in modelling a fuzzy logic system is >uzzification, and this is used to
convert each of input data to a degree of membership function in the 9:*=:7 fuzzy logic
toolbox *hus, the triangular membership function is chosen for fuzzification of both inputs
and output variables !n the process of fuzzification, each input data was mapped with the set
of rules to establish the degree of fitness on how each rule matches the particular input !t is
to be noted that the triangular membership function was used to map the degree of
membership of the labels of each variables used for input and output variables
*he schematic representation of the fuzzy logic system for *+D9 disease risk predicting
system in figure & below shows the set of variables used as inputs ofr the model and the
risk as the output variable for the system
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T2DM DISEASE RISK INFERENCE ENGINE
Using
l#$% &'D (o)y-ass-In)$* ! +al#$% &'D (&g$ ! +al#$% &'D (History-of-,$stational-Dia$t$s ! +al#$% &'D (loo)-.r$ss#r$ ! "al#$% &'D (,$n)$r ! +al#$% TH/' (
#l$ 1
#l$ 2
#l$ 3
#l$ '
#l$ '
&TI' (Us$ Triangular Membership Function to ma t$ +arial$s to t$ir r$s$ti+$ la$l
Famil !istor o" Diabetes
#o$ Mass In$e% MI'
Age
!istor o" Gestational Diabetes
#loo$ (ressure
Gen$er
AGGREGATI)N
&&ll #l$s ar$ aggr$gat$) into a singl$ f#i:$) o#t#t +arial$Ty$ 2 Dia$t$s $llit#s is .r$)i
(;o
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.( S#mulat#on En%#ronment
*he schematic representation of the *ype + Diabetes 9ellitus Disease 6isk inference engine
shown in the figure & above shows different factors of *+D9 as input to the fuzzy logic
model for determining the risk of the disease (*ype + Diabetes 9ellitus), and the output
variable is determined by the fuziffication of the input variables using *riangular
9embership function to map the variables to their respective label *able & shows the
description of the fuzzification of input variables for *+D9 Disease using the mathematical
model in equation & to plot the fuzzified values and equations and 2 for variables
with two labels, while equations 4, 1, and . for variables with three labels *able +
shows the fuzzification of the input variables (ie risk factors) needed for determining the
risk of *ype + Diabetes 9ellitus disease
*he simulation environment for the *ype + Diabetes 9ellitus Disease risk predicting system
was carried out using the 9:*=:7 :! *he formulation of the model was done by using
the 9:*=:7 fuzzy logic toolbox *he 9:*=:7 fuzzy logic toolbox contains fuzzy
inference system (>!) editor that was used to define both the input and output variables
*he input variables consist of six (1) input labels with three () or two (+) triangular
membership function as shown in figure + below, while the output variables consist of
membership functions *he rule editor interface was used for the rule-based of the interface
inference engine of >! showing the relationship between the six (1) input variables and the
output variables using !> " *$/< rules *he :
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Table .2 The 7u@@#f#cat#on of T2) #&ea&e R#&k /n!ut 5R#&k 7actor6
S>N R#&k 7actor& abel& 7u@@' og#c alue
& >amily $istory of *+D9
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>igure + >uzzy !nference ystem for rediction of risk of *+D9
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.* S'&tem Reu#rement
ystem requirements are an important aspect of system development and they are used to
specify details of system functions, services and the basis for designing the system *hese
requirements were used to discover and clarify the function of the system *his segment
consists of the feasibility study, the specification and analysis of requirements, and pro0ect
definition *herefore, the scope of the system requirement of the predicting system covers
the following areasG
i !dentify the factors affecting diabetes mellitus disease and their corresponding
influence *his is to highlight the factors that are considered to be associated with
diabetes mellitus disease and how significance their influence is so that an accurate
predictive model can be formulatedii 6epresent and document the activities to be carried out by the type + diabetes
mellitus disease risk predicting system and the corresponding entities :fter
identification of the factors and their corresponding influences, there is a need to
represent the activities and entities involved with the system and document the
informationiii Henerate a model using fuzzy logic approaches *he model was developed by
identifying the variables that are required in type + diabetes mellitus diseaseiv Develop a prototypical type + diabetes mellitus disease risk predicting system with
using fuzzy set approaches /fforts were made to ensure that the system is able to
predict the likelihood of occurrence of diabetes mellitus disease
*he system aim to assist doctor in predicting the patient type + diabetes mellitus disease risk
status thereby reduces the number of people coming to the hospital and easing the doctor?s
task !t will also allow people to know how prone they are to developing type + diabetes
mellitus disease without visiting the hospital based on their body mass index, blood
pressure, sedentary lifestyle, health history and their current health status, though some
information will still be needed from the doctor for accurate prediction
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., S#mulat#on Tool&
>or the simulaton of the proposed model, the >uzzy =ogic *oolbox available in the
9:*=:7 6+&a software will be used *he 9:*=:7 >uzzy =ogic *oolbox consist of
>! editor, 9embership >unction /ditor, 6ule /ditor, 6ule Ciewer, urface Ciewer, and the
>uzzy !nference ystem (>!) at the centre of the whole system (Mathworks, 2013).
i >uzzy !nference ystem /ditor3 *he 9:*=:7 fuzzy logic toolbox contains fuzzy
inference system (>!) editor that was used to define both the input and output
variables *he >! /ditor handles the high-level issues for the system by determining
the number of input and output variables alongside their names *he >uzzy =ogic
*oolbox does not limit the number of inputs $owever, the number of inputs may be
limited by the available memory of the machine !f the number of inputs is too large,
or the number of membership functions is too big, then it may also be difficult to
analyse the >! using the other HI! tools##. *he 9embership >unction /ditor is used to define the shapes of all the membership
functions associated with each variable###. *he 6ule /ditor is for editing the list of rules that defines the behaviour of the
system#%. *he 6ule Ciewer and the urface Ciewer are used for looking at, as opposed to
editing, the >! *hey are strictly read-only tools *he 6ule Ciewer is a 9:*=:7
based display of the fuzzy inference diagram shown at the end of the last section
Ised as a diagnostic, it can show (for example) which rules are active, or how
individual membership function shapes are influencing the resultsv *he urface Ciewer is used to display the dependency of one of the outputs on any
one or two of the inputs R that is, it generates and plots an output surface map for
the system
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7#gure . 70DD? O:/C TOOBO
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.; S'&tem O!erat#onal Reu#rement&
.;.1 Hard9are Reu#rement&
>or the proper functioning of the diabetes mellitus disease risk prediction system, the
following items will be needed for the hardwareG
a : ;omputer with internet access and at least a entium !!! processor@
b :n input and pointing device@
c : hard Disk of at least &H7 of size is required in order for the repository to run well
without congesting other programs@ and
d 6andom :ccess 9emory of at least 4&+97 is required
.;.2 Soft9are Reu#rement&
*he following software will be needed for the proper functioning of the diabetes mellitus
disease risk prediction systemG
a) #indows %perating ystem (#ins . and above)
a) 9:*=:7 >uzzy =ogic *oolbox
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CHAPTER 7O0R
T?PE 2 /ABETES )E/T0S /SEASE R/SF )OE EEOP)ENT
".1 S#mulat#on of the 7u@@' og#c )odel for Pred#ct#ng T'!e 2 #abete& )ell#tu&
#&ea&e R#&k
*he simulation of the fuzzy logic model for the prediction of *ype + Diabetes 9ellitus
disease risk was simulated using the fuzzy logic toolbox available in the 9:*=:7 +&
Development /nvironment Ising the formulated triangular membership functions defined
for each input and output variable, the membership functions and the respective fuzzy
inference model for the risk of *+D9 using six risk factors as shown in figure + above
were used as the inputs *he triangular membership functions in figures
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>igure 2G 9embership function of >amily $istory
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>igure 2&G *riangular 9embership >unction of :ge
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>igure 2+G *riangular 9embership >unction for 7ody 9ass !ndex
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>igure 2G *riangular 9embership >unction for $istory of Hestational Diabetes
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>igure 22G *riangular 9embership >unction for 7lood ressure
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>igure 24G *riangular 9embership >unction for Hender
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>igure 21G *riangular 9embership >unction for the risk of *+D9
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RE7ERENCES
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:kinkugbe %% *he non-communicable diseases in
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;hinenye , Boung / (+&&) YDiabetes ;are !n amuyiwa %%,
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>Srch, Aristine Hender and *+D9 UinternetV +&2 :ug &@ Diapedia &2'.+8&1 rev no
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=iu, *->, ung, #-A, X 9ittal, : (+1) Y9odel gene network by semi-fixed 7ayesian
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sub-aharan :frican originG clinical pathophysiology and natural history of beta-cell
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