Modification of the Integrated Sasang Constitutional...

Research ArticleModification of the Integrated Sasang ConstitutionalDiagnostic Model

Jiho Nam,1 Jun-Su Jang,2 Honggie Kim,3 Jong Yeol Kim,2 and Jun-Hyeong Do2

1Medizen Humancare Inc., 20F Keungil Tower, 223 Teheran-ro, Seoul, Republic of Korea2KM Fundamental Research Division, Korea Institute of Oriental Medicine, 1672 Yuseong-daero, Daejeon, Republic of Korea3Department of Information and Statistics, Chungnam National University, 99 Daehak-ro, Daejeon, Republic of Korea

Correspondence should be addressed to Jong Yeol Kim; [email protected] and Jun-Hyeong Do; [email protected]

Received 26 June 2017; Accepted 17 October 2017; Published 27 November 2017

Academic Editor: Bhushan Patwardhan

Copyright © 2017 JihoNam et al.This is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

In 2012, the Korea Institute of Oriental Medicine proposed an objective and comprehensive physical diagnostic model to addressquantification problems in the existing Sasang constitutional diagnosticmethod.However, certain issues have been raised regardinga revision of the proposed diagnostic model. In this paper, we propose various methodological approaches to address the problemsof the previous diagnostic model. Firstly, more useful variables are selected in each component. Secondly, the least absoluteshrinkage and selection operator is used to reduce multicollinearity without the modification of explanatory variables. Thirdly,proportions of SC types and age are considered to construct individual diagnostic models and classify the training set and the testset for reflecting the characteristics of the entire dataset. Finally, an integrated model is constructed with explanatory variables ofindividual diagnosis models. The proposed integrated diagnostic model significantly improves the sensitivities for both the maleSY type (36.4% → 62.0%) and the female SE type (43.7% → 64.5%), which were areas of limitation of the previous integrateddiagnostic model. The ideas of these new algorithms are expected to contribute not only to the scientific development of Sasangconstitutional medicine in Korea but also to that of other diagnostic methods for traditional medicine.

1. Introduction

In recent years, the interest in and use of constitutional healthcare services have been reported to increase consistently.According to a survey by the Korea Institute of OrientalMedicine (KIOM), the percentage of constitutional healthservices in total traditionalmedical services reached 30.7% in2015, which is approximately 7% higher than the correspond-ing value of 23.8% in 2004 [1].

Sasang constitutional medicine is a personalized med-icine that diagnoses the patient’s constitution as one of thefour Sasang constitutional (SC) types (Tae-Yang: TY, Tae-Eum: TE, So-Yang: SY, and So-Eum: SE) and treats him/herdifferently depending on the constitution.Therefore, a precisediagnosis of constitution is central to producing consistentresults when various experts diagnose the constitution of asingle person.

From 2007 to 2011, KIOM collected face, body, voice, andquestionnaire data from 2,773 subjects who were diagnosedin terms of constitutional prescriptions by Sasang constitu-tional experts in 23 oriental medical clinics. Based on thesedata, an objective and comprehensive Sasang constitutionaldiagnosticmodel, which has addressed the existing problems,was proposed in 2012 [2].

Based on this model, many studies on the relationshipbetween Sasang constitution and disease have been con-ducted. For example, the SY and TE types are independentrisk factors for nonalcoholic fatty liver disease [3] regard-less of obesity level, TE is a strong risk factor for type 2diabetes [4], and metabolic syndrome increases the risk ofcardiovascular disease in certain physical conditions, suchas the SY type [5]. Additionally, physiological characteristicsmay differ across Sasang types: the SY type has a highervalue of total nasal resistance compared to the TE and SE

HindawiEvidence-Based Complementary and Alternative MedicineVolume 2017, Article ID 9180159, 8 pageshttps://doi.org/10.1155/2017/9180159

https://doi.org/10.1155/2017/9180159

2 Evidence-Based Complementary and Alternative Medicine

Table 1: The number of excluded and included data cases for each individual diagnostic model.

Reason for exclusion(total number of subjects: 3,849) Face Body shape Voice Questionnaire

Not collected initially 182 0 592 624TY constitution 76 76 73 73Below 15 years of age 96 107 79 79Data extraction errors and missing cases 1,343 429 388 150Final number of subjects 2,152 3,237 2,717 2,923Common samples after integrating the four components(for the integrated diagnostic model) 1,891 (male: 657; female: 1,234)

Table 2: Distribution of subjects by age group.

Age groupFace Body shape Voice Questionnaire Common samples

TE SE SYM∗ F∗∗ M F M F M F M F M F M F

10s 32 29 50 41 45 40 46 41 10 14 13 6 6 820s 89 162 120 234 97 198 98 214 23 45 27 50 25 4830s 131 273 186 375 159 326 165 346 46 66 35 81 32 9340s 161 317 245 453 202 381 214 416 58 109 42 73 41 9550s 169 317 264 464 219 375 240 412 61 99 34 72 49 10560s∼ 171 301 299 506 244 431 267 464 66 128 22 68 67 74Total 2,152 3,237 2,717 2,923 1,891∗M: male; ∗∗F: female.

types [6], and the TE type may tolerate psychological oroxidative stress better than the other types [7]. Other studieson constitution-specific physiology, such as comparisons ofgut microbiota among Sasang constitutional types [8], havealso been conducted using a constitutional diagnostic model.

As increasingly more researchers use the diagnosticmodel developed by KIOM, multiple issues have also beenraised.

Firstly, the ratio of predicted SC types should be the sameas the proportions of actual SC types. In the existing model,the predicted proportion of the TE type is much higher thanthe actual one, which, we believe, is due to the fact that theproportion of TE type is the highest.

Secondly, the multicollinearity problem results from thefact that the explanatory variables of the diagnostic model arehighly correlated, making the regression coefficient estimatesunreliable.

Finally, in the existing diagnostic model, it was assumedthat all individual diagnostic components equally contributeto the diagnosis of the SC types, which turns out to besomewhat unreasonable.

Therefore, in this paper, we propose a novel modifiedmodel to solve the problems of the previous diagnosticmodel,and we compare the revised model with the previous modelin terms of various aspects.

2. Methods

2.1. Participants and Data Acquisition. Using the same stan-dard operating procedure used in the previous study, we

collected more face, body shape, voice, and questionnairedata from the subjects than the previous study. A total of3,849 patients, ranging from teenagers to people in theireighties, were recruited from 23 sites (oriental medical clin-ics) between November 2006 and August 2012. This processwas approved by the KIOM Institutional Review Board (I-0910/02-001) andwe obtainedwritten informed consent fromthe subjects. As in the previous study [2], several patientswereexcluded for a variety of reasons, such as a small numberof TY-type subjects, subjects below the age of 15 havinggrowth spurts, or improper data (Table 1). Table 2 shows thedistribution of subjects by age group.

In comparison with the previous study, the data for oneyear (August 2011 to August 2012) have been added, and thedata extraction algorithm has been enhanced to increase theusage.

2.2. Candidate Feature Variables

2.2.1. Facial Images. A total of 57 facial feature points,including 13 newly added points over the previous study, wereextracted using an automatic feature extraction algorithm(Figure 1). However, the feature points of the upper eyelidline, which require a high-resolution image, were excludedfor more efficient implementation. Facial candidate featurevariables using the extracted points are described in Table 3.

2.2.2. Body Shape. In contrast to the previous study, weexcluded both BMI and body weight and reconstructed themodel with the remaining variables. The reason for this

Evidence-Based Complementary and Alternative Medicine 3

17 52255333 26

94 3638

50

9190

43 143

81 136138

194

133126118 153

125

73

77

9

6

12

1421

9190

7271

7

117

18

FArea02

FArea03

47∗

10∗

10∗

84∗

13∗

87∗88∗

22∗

25∗

32∗ 33∗

36∗

110∗

y

x

1 ＝Ｇ

1 ＝Ｇ

1 ＝Ｇ

Figure 1: Feature points extracted automatically from facial images. ∗Added feature point.

exclusion is that there is a prejudice to diagnose as a TE typeif the BMI and body weight are high and to diagnose as anSE type if BMI and body weight are low. Moreover, in theliterature, it is noted that the body shape of the TE type istall and large rather than obese, and the body shape of the SEtype is short and small rather than slender [9].

2.2.3. Voice. Vocal features were extracted using a C++program combined with the hidden Markov model toolkit[10]. Input vocal signals were divided into multiple windowscorresponding to the reference time duration for featureextraction. The window size was 46.4ms and was mapped to211 samples at a 44.1 kHz sampling frequency.

The voice signal used was a recording of five vowels andone sentence repeated twice. Unlike the previous study [2],feature extraction was performed only from the sentence,excluding the vowels. Generally, the sentence was moresuitable than the vowels because of the unchanged voiceinformation for characterizing an individual. The accuracyand repeatability of the Sasang constitution diagnosis werebetter in the case of using only the sentence compared to thecase of using both the vowels and the sentence [11].

A description of vocal features is provided in Table 4.The harmonic-to-noise ratio (HNR) and the cepstral peakprominence (CPP) are newly added features not found in ourprevious study [2].

TheHNR is ameasure that quantifies the amount of addi-tive noise in the voice signal. It is widely used to characterizehealthy and disordered voices. The CPP is known to be anaccurate predictor and a more reliable measure of dysphoniathan other vocal features, such as jitter, shimmer, and HNR[12].

2.2.4. Questionnaire. Binary variables were constructedusing the response categories of the questions in the

questionnaire, which consisted of multiple-choice questionsin Supplementary Table S1 in Supplementary Materialavailable online at https://doi.org/10.1155/2017/9180159. Theprocedure of generating questionnaire continuous variablesis summarized in Supplementary Figure S1.

2.2.5. Compensating for Age Differences. As in previous study,because the candidate feature variables may have shown age-specific trends, a process to eliminate the effect of age wasconsidered by normalizing each candidate feature variablewith moving average and standard deviation of the variablefor the given age [2].

2.3. Model for Sasang Constitutional Diagnosis

2.3.1. Individual Diagnostic Models. The regression coeffi-cients of the explanatory variables used in the previous indi-vidual models were estimated using ordinary least squares(OLS). However, the OLS estimator may be acquired witha large variance of the coefficients and be inestimable if thedimension of explanatory variables is too high or each ofthem is highly intercorrelated. These problems are referredto as overfitting and multicollinearity, respectively. One well-known solution to these problems is the least absoluteshrinkage and selection operator (LASSO), which shrinks thevariance of the coefficients and makes other coefficients zero[13]. Coefficients were estimated using the glmnet packageimplemented in the R software. The tuning parameter 𝜆 wasselected from the result of 10-fold cross-validation using themean square error (MSE) to measure the risk of loss. Thedecision rule is to choose log(𝜆) that gives the minimummean cross-validated error.

In the questionnaire model, we calculated questionnairecontinuous variables with binary variables using LASSO(Supplementary Table S5) and then we constructed question-nairemodel with continuous variables usingOLS because the

https://doi.org/10.1155/2017/9180159


Table3:Ca

ndidatefacialfeature

varia

bles.

Size-related

varia

bles

Shape-related

varia

bles

Face

shape

(i)Width

FD43

143,FD

53153,FD

94194,FD

H33

133

(i)Ang

leFA

5394,FAs153194,FA

9443,FAs194143,FA

5394

43,FA

1825

43,FA

118125143,

(ii)D

epth

PDH

4453,P

DH

1236,P

DH

1436,P

DH

2136

FA18

1743,FA

118117

143,FA

1725

43,FA

117125143,FA

1825

94,FA

1843

50,

FA18

9450

(iii)Height

FDV

4752,FDV

4750,FDV

1021,FDV

5250,FDV

8150,

(ii)R

atio

FHD

3313343

143,FD

D53

15343

143,FD

D94

19443

143,FH

D33

13353

153,

PDV

612,P

DV

632,P

DV

1032,P

DV

1232,P

DV

2132,P

DV

2532

FVV

4752

5281,FVV

4752

5250,FVV

4752

8150,FVV

5281

8150,P

VV

610

1033,

(iv)A

rea

FArea02,FA

rea03,FA

rea02/FA

rea03

PVV

1214

1432,P

VV

1221

1432,FVD

5250

53153,FV

D52

8153

153,FV

D81

5094

194

Forehead

Height:

FDV

4710,P

DV

69,PD

V610

(i)Ang

lePA

i76,PA

i97,PA

i7172,PAi7

273,PA

679

(ii)R

atio

PDD

77969

(iii)Depth

PDH

67,PD

H912

(iv)D

istance

PD777,P

DV

67,PD

V79,PD

V912,PA

912,PA

1012

Eye

(i)Width

FDH

1825,FDH

118125,FD

H18

118,FDH

25125,FD

H21

121

(i)Ang

leFA

1817

25,FA

118117

125,FA

i2517,FAis125117

,FAis18

17,FAi118

117,

FAis18

25,FAi118

125

(ii)H

eight

FD17

26,FD

117126

(ii)R

atio

FDH

1726

1825,FDH

117126118

125,FD

H52

5018

118,FDD

1726

5281,FDD

1726

5250,

(iii)Distance

FD17

25,FD

117125,FD

1825,FD

118125

(FDH

1825+F

DH

118125)/2/FDH

18118

,(FD

H18

25+F

DH

118125)/2/FD

53153,

(FDH

1825+F

DH

118125)/2/FDH

33133,FH

D18

11853

153,FH

D25

12553

153

Nose

(i)Width

FDH

36136,PD

H12

14,P

DL14

1221

(i)Ang

lePA

i1412,PA

1421,PA

1214

21,PAi1384,PA

8788,PA

8721

(ii)H

eight

FDV

5281,P

DV

1214,P

DV

1421,P

DV

1221,P

D12

21(iii)Depth

PDH

4121

(ii)R

atio

FHD

3613653

153,FV

H52

8136

136

(iv)A

rea

FArea52

36136,PA

rea12

1421

Mou

th(i)

Width

PDL22

2132,P

DL25

2132

Ratio

FVV

8050

5250,FVV

8050

8150

(ii)H

eight

FDV

8050

Chin

(i)Depth

PDH

3236

Ang

lePA

3233,PA

3336,PA

3233

36(ii)H

eight

PDV

3236

(iii)Distance

PD32

36

Note.FD(𝑛1,𝑛2)[orP

D(𝑛1,𝑛2)]:distance

betweenpo

ints𝑛1and𝑛2in

afrontal(orp

rofile)im

age;

FDH(𝑛1,𝑛2)[orP

DH(𝑛1,𝑛2)]:horizon

tald

istance

between𝑛1and𝑛2in

afrontal(orp

rofile)im

age;

FDV(𝑛1,𝑛2)[orP

DV(𝑛1,𝑛2)]:vertic

aldistance

between𝑛1and𝑛2in

afrontal(orp

rofile)im

age;

FA(𝑛1,𝑛2)[orP

A(𝑛1,𝑛2)]:ang

lebetweenthelinethrou

ghtwopo

ints𝑛1and𝑛2andah

orizon

tallineinafrontal(orp

rofile)im

age;

FA(𝑛1,𝑛2,𝑛3)[orP

A(𝑛1,𝑛2,𝑛3)]:ang

lebetweenthreep

oints,𝑛1,𝑛2,and𝑛3,inafrontal(orp

rofile)im

age;

PAR(𝑛1,𝑛2,𝑛3):area

ofthetria

ngleform

edby

threep

oints,𝑛1,𝑛2,and𝑛3,inap

rofileimage;

FHD𝑛1𝑛2𝑛3𝑛4[orP

HD𝑛1𝑛2𝑛3𝑛4]=

FDH𝑛1𝑛2/FD𝑛3𝑛4[orP

DH𝑛1𝑛2/PD𝑛3𝑛4];

FDH𝑛1𝑛2𝑛3𝑛4[orP

DH𝑛1𝑛2𝑛3𝑛4]=

FD𝑛1𝑛2/FDH𝑛3𝑛4[orP

D𝑛1𝑛2/PDH𝑛3𝑛4];

FDD𝑛1𝑛2𝑛3𝑛4[orP

DD𝑛1𝑛2𝑛3𝑛4]=

FD𝑛1𝑛2/FD𝑛3𝑛4[orP

D𝑛1𝑛2/PD𝑛3𝑛4];

FVD𝑛1𝑛2𝑛3𝑛4[orP

VD𝑛1𝑛2𝑛3𝑛4]=

FDV𝑛1𝑛2/FD𝑛3𝑛4[orP

DV𝑛1𝑛2/PD𝑛3𝑛4];

FVV𝑛1𝑛2𝑛3𝑛4[orP

VV𝑛1𝑛2𝑛3𝑛4]=

FDV𝑛1𝑛2/FDV𝑛3𝑛4[orP

DV𝑛1𝑛2/PDV𝑛3𝑛4];

FVH𝑛1𝑛2𝑛3𝑛4[orP

VH𝑛1𝑛2𝑛3𝑛4]=

FDV𝑛1𝑛2/FDH𝑛3𝑛4[orP

DV𝑛1𝑛2/PDH𝑛3𝑛4].


Table 4: Description of sentence features.

Sentence feature∗ DescriptionsF0, sFCV Average pitch frequency and coefficient of variation of the pitchsF10, sF50, sF90 10th, 50th, and 90th percentiles of pitch distributionsFHL Ratio of (sF90-sF50) to (sF50-sF10)sDT Duration time of a sentence readingsHNR HNRsCPP CPPsMFCC1∼12 12 Mel-frequency cepstral coefficients∗All feature values are based on the averaged output of the two sentence utterances, which were repeated recordings of the same sentence.

dimension of explanatory variables is low and each of them isnot highly intercorrelated.

In addition, because the ratio of SC types is not uniform,the tendency in the previousmodel was to classify the TE typeas the highest ratio. As a result, the sensitivity of TE type washigh, but the sensitivity of other SC types was significantlylow.

Sensitivity was low, particularly in the male SY type andfemale SE type. To compensate for this problem, weighting bySC types was added to the model by considering the ratios ofSC types.

In the previous model, the training and test sets wereseparated based on the data collection year. However, therewas a problem that the two groups are heterogeneous, whichcame from the effect of differences due to the collection year.In the present model, the complete set was randomly dividedinto a training set and a test set at a ratio of 7 : 3 consideringSC type and age.

The results of estimated coefficients for face, voice, bodyshape, and questionnaire continuous features for each SCtype are shown in Supplementary Tables S2, S3, S4, and S6,respectively.

2.3.2. Integrating Diagnostic Models from Four DiagnosticComponents. Let 𝜋𝑖𝑗 be the estimated probability of the 𝑖thsubject in category 𝑗 for each individual diagnostic model,where 𝑗= 1, 2, and 3 indicate TE, SE, and SY type, respectively.

In the previous study, the importance of each individualdiagnostic model was also considered through multiplyingthe weights by 𝜋𝑖𝑗. The integrated estimated probability of anSC type 𝑗 for the 𝑖th subject, denoted as TSCORE𝑖𝑗, can bedefined by the sum of (𝜋𝑖𝑗)𝑟 with weight 𝑤𝑟:

TSCORE𝑖𝑗 =4

∑𝑟=1

𝑤𝑟 (𝜋𝑖𝑗)𝑟 , (1)

where 𝑟 indicates each individual diagnostic component; 𝑟 =1, 2, 3, and 4 represent face, body shape, questionnaire, andvoice, respectively.

In this study, we constructed a model with explanatoryvariables as (𝜋𝑖𝑗)𝑟 of individual diagnosismodels.This schemecan be regarded as weighting by a methodical and rigorousmethod, unlike the case of the previous study, in whicharbitrary weights were set.

In addition, compared to the previous weight𝑤𝑟 assignedto each individual diagnostic component, the weight 𝑤𝑗𝑟 of

this study was obtained considering each component and SCtype as shown in Supplementary Table S7:

TSCORE𝑖𝑗 =4

∑𝑟=1

𝑤𝑗𝑟 (𝜋𝑖𝑗)𝑟 . (2)

Finally, the predicted SC type for the 𝑖th subject wasdetermined in the same way as in the previous study.

Predicted SC𝑖

= argmax (TSCORE𝑖1,TSCORE𝑖2,TSCORE𝑖3) ,(3)

where the numbers of the subscripts indicate the TE, SE, andSY types.

3. Results

The predicted results of the proposed integrated diagnosticmodel are shown in Table 5. Relative to the predictions ofthe previous integrated diagnostic models, the accuracy inthe test set is improved by approximately 10% on average.Moreover, the sensitivities of the male SY type (36.4% →62.0%) and the female SE type (43.7%→ 64.5%), which werelow in the previousmodel, were significantly improved, whilethe sensitivity of the TE type and that of the female SY typewere somewhat lowered.

Table 6 shows the results of applying a cutoff to the pre-dicted results of the proposed integrated diagnosticmodel. Toextract more typical SC-type predictions, the reference valuefor the cutoff criterion was changed to the maximum valueminus the second highest value from the maximum valueamong the three probability values of the SC types, which wasproposed in the previous study. This change was determinedafter implementing various cutoff value transformations.

Comparing the performances before and after the cutoff,the sensitivities and accuracies of each SC typewere improvedby approximately 7% on average. In particular, the sensitivityof the female SE type improved significantly.

This result alone does not make the comparison withthe previous model justified due to the fact that it is not acomparison for the same test set. In this study, the number ofdata cases is greater and the complete dataset is divided intothe training set and test set at random. To make an accuratecomparison of the present model with the previous one, anadditional comparisonwas performedwith only the common


Table 5: Diagnostic results of the proposed integrated diagnostic model.

Male FemalePredicted SC type Sensitivity Predicted SC type Sensitivity

TE SE SY Total TE SE SY TotalTraining set

True SC typeTE 144 14 30 188 76.6% 223 32 66 321 69.5%SE 14 81 26 121 66.9% 33 146 63 242 60.3%SY 29 28 92 149 61.7% 65 65 168 298 56.4%Total 187 123 148 458 321 243 297 861

Accuracy 69.2% 62.4%Test set


Accuracy 71.9% 65.7%

Table 6: Diagnosis results of proposed integrated diagnosis model using a cutoff.


TE SE SY Total TE SE SY TotalTraining set


Accuracy 75.2% 69.2%Test set



part of the data that were included in finding both modelsamong the test sets used in this model (Table 7).

A comparison of the two results shows that the sensitiv-ities of the previous model are greater for the male TE typeand the female SY type, but the sensitivities and accuracies ofthe other SC types are higher than average by more than 10%.

Although the result of the previous study has an advan-tage because some training data from the previous study areincluded in the test set of this study, it is notable that theoverall performance of the proposed model is significantlyimproved.

4. Discussion and Conclusions

Relative to the previous studies, the diagnostic performanceis significantly improved because of the reasonable considera-tions of variousmethodological approaches and the increasednumber of data cases and variables used to construct themodel.

Although the previous diagnostic model offers the sameaccuracy as the overall model, the sensitivity for the TEtype, which has the highest ratio among the SC types, is thehighest, whereas the sensitivities for the SE and SY types are


Table 7: Comparison of diagnosis results of the set of common data between the test set of the proposed integrated diagnosis model and thefull set of the previous integrated diagnosis model.


TE SE SY Total TE SE SY TotalProposed integrated diagnosis model


Accuracy 69.2% 66.5%Previous integrated diagnosis model



significantly lower. This result occurs because the estimationof the regression coefficient of the model is focused onreducing the error for the TE type. Therefore, to improvethe performance for the SE and SY constitutional types,although the diagnostic performance for the TE type issomewhat degraded, the regression coefficients of the modelare estimated with equal weight by training the model withdifferent weights for each SC type based on their proportions.The TE type, which was frequently predicted in the previousmodel, was confirmed to have inferior performance, butthe performance associated with the other SC types wasimproved.

As the regression coefficients are difficult to analyze indi-vidually because the model consists of multiple explanatoryvariables, the explanatory variables used in this model arehighly correlated because the calculated variables are fromsimilar positions on the body.

In general, the width of a confidence interval of an esti-mated regression coefficient increases whenmulticollinearityoccurs. The fact that the confidence interval is wide impliesthat the estimated regression coefficient value is not actuallyconfirmed but is highly likely to differ from the true valueof the regression coefficient. Therefore, the influence of thevariable cannot be explained correctly, and, as a result, thereliability of the model itself deteriorates.

Among the methods that can solve the multicollinear-ity problem, LASSO, as used in this study, can reducemulticollinearity without the modification of explanatoryvariables. Although the resolution of multicollinearity doesnot lead to improved accuracy, the results suggest that thereliability of the regression coefficient is satisfiable and thatstable results will be obtained when new data are analyzedusing the model.

In the previous study, the training and test sets were clas-sified based on the data collection year, but the proportionsof SC types and age may be heterogeneous in the trainingand test sets. Therefore, considering the proportions of SCtypes and age, it is reasonable and well reflective of thecharacteristics of the entire dataset to obtain both the trainingset and the test set from the complete dataset.

In traditional medicine in Asia, including Chinesemedicine, facial, body shape, voice, and pulse information,as well as ordinary symptoms, are combined in the processof diagnosis for prescription decisions. In particular, manystudies on the relationship between Prakriti (Ayurveda con-stitution) and various objective biological parameters havebeen actively conducted in the field of Ayurveda, and, atthe same time, efforts are being made to propose a standardprotocol for the objective and reliable diagnosis of Prakriti[14]. We believe that the proposed method is the mostadvanced tool to support the ability of Asian doctors todiagnose in an objective and scientific way.The improvementof this algorithm is expected to contribute not only to thescientific development of Sasang constitutional medicine inKorea but also to that of other traditional medical diagnosismethods.

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this article.

Acknowledgments

This research was supported by the Bio & Medical Technol-ogy Development Program of the NRF funded by the Koreangovernment, MSIP (NRF-2015M3A9B6027138).


References

[1] J. Y. Kim and D. D. Pham, “Sasang constitutional medicine as aholistic tailoredmedicine,” Evidence-Based Complementary andAlternative Medicine, vol. 6, supplement 1, pp. 11–19, 2009.

[2] J.-H. Do, E. Jang, B. Ku, J.-S. Jang, H. Kim, and J. Y. Kim, “Devel-opment of an integrated Sasang constitution diagnosis methodusing face, body shape, voice, and questionnaire information,”BMC Complementary and Alternative Medicine, vol. 12, pp. 85–93, 2012.

[3] S. K. Lee, D. W. Yoon, S. W. Lee, J. Y. Kim, J. K. Kim,and C. Shin, “Non-alcoholic fatty liver disease among sasangconstitutional types: a population-based study in Korea,” BMCComplementary and Alternative Medicine, vol. 15, no. 1, articleno. 399, 2015.

[4] N.H.Cho, J. Y. Kim, S. S. Kim, S. K. Lee, andC. Shin, “Predictingtype 2 diabetes using Sasang constitutionalmedicine,” Journal ofDiabetes Investigation, vol. 5, no. 5, pp. 525–532, 2014.

[5] N. H. Cho, J. Y. Kim, S. S. Kim, and C. Shin, “The relationshipof metabolic syndrome and constitutional medicine for theprediction of cardiovascular disease,” Diabetes & MetabolicSyndrome: Clinical Research & Reviews, vol. 7, no. 4, pp. 226–232, 2013.

[6] D.W. Yoon, S. K. Lee, H. Yi et al., “Total nasal resistance amongSasang constitutional types: a population-based study inKorea,”BMC Complementary and Alternative Medicine, vol. 13, 2013.

[7] H. G. Kim, Y. J. Kim, Y. C. Ahn, and C. G. Son, “Serumlevels of stress hormones and oxidative stress biomarkersdiffer according to sasang constitutional type,” Evidence-BasedComplementary and Alternative Medicine, vol. 2015, Article ID737631, 2015.

[8] B.-S. Kim, H. S. Bae, C.-Y. Lim et al., “Comparison ofgut microbiota between sasang constitutions,” Evidence-BasedComplementary and Alternative Medicine, vol. 2013, Article ID171643, 2013.

[9] H. Chae and Y. Kown, “Best-fit index for describing physicalperspectives in Sasang typology,” Integrative Medicine Research,vol. 4, no. 1, pp. 20–28, 2015.

[10] S. Young, G. Evermann, T. Hain et al., The HTK Book, 2002,http://htk.eng.cam.ac.uk/.

[11] J.-S. Jang, Y.-S. Kim, B. Ku, and J. Y. Kim, “Recent progress invoice-based sasang constitutionalmedicine: Improving stabilityof diagnosis,” Evidence-Based Complementary and AlternativeMedicine, vol. 2013, Article ID 920384, 2013.

[12] Y. D. Heman-Ackah, R. J. Heuer, D. D. Michael et al., “Cepstralpeak prominence: A more reliable measure of dysphonia,”Annals of Otology, Rhinology & Laryngology, vol. 112, no. 4, pp.324–333, 2003.

[13] R. Tibshirani, “Regression shrinkage and selection via thelasso,” Journal of the Royal Statistical Society: Series B (StatisticalMethodological), vol. 58, no. 1, pp. 267–288, 1996.

[14] S. Bhalerao and K. Patwardhan, “Prakriti-based research:Good reporting practices,” Journal of Ayurveda and IntegrativeMedicine, vol. 7, no. 1, pp. 69–72, 2016.

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