Web Mining Final Report

Final Report How likely Am I to have Diabetes 04/28/2015

FINAL REPORT

WEB MINING

Ashwin Kumar Pitchai (akp73) & Suchait Mattoo (sm925)


ContentsDescription..................................................................................................................................................3

Questions....................................................................................................................................................3

Data Dictionary............................................................................................................................................4

Sample Data................................................................................................................................................5

Outlier Detection.........................................................................................................................................6

Normalized Data..........................................................................................................................................6

Association Rules.........................................................................................................................................7

Performance Models...............................................................................................................................8

Naïve Bayes.............................................................................................................................................9

Neural Network.....................................................................................................................................10

SVM.......................................................................................................................................................11

Logistic Regression................................................................................................................................12

KNN.......................................................................................................................................................13

Evaluation Models:....................................................................................................................................14

Decision Tree:............................................................................................................................................15

Naïve Bayes...........................................................................................................................................15

Neural Net:................................................................................................................................................16

SVM.......................................................................................................................................................16

Logistic Regression................................................................................................................................16

K Nearest Neighbor:..................................................................................................................................17

Answers.....................................................................................................................................................18

References.................................................................................................................................................18



Description:

Our dataset relates whether a patient shows signs of diabetes according to World Health Organization criteria (i.e., if the 2 hour post-load plasma glucose was at least 200 mg/dl at any survey examination or if found during routine medical care).

The population lives near Phoenix, Arizona, USA. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. Each instance represents individual patients and their various medical attributes along with diabetes classification attributes.

Questions:

How likely is a particular patient affected by diabetes given the medical parameters?



Data Dictionary

Attribute Description Data Type RangePregnancies Number of

PregnanciesNumeric(17) 0-17

PG Concentration

Plasma glucose at 2 hours in an oral glucose tolerance test

Numeric(199) 0-199

Diastolic BP Diastolic Blood Pressure (mm Hg)

Numeric(122) 0-122

Tri Fold Thick Triceps Skin Fold Thickness (mm)

Numeric(52) 0-52

Serums Ins 2-Hour Serum Insulin (mu U/ml)

Numeric(846) 0-846

BMI Body Mass Index: (weight in kg/ (height in m)^2)

Decimal(53.2) 0-53.2

DP Function Diabetes Pedigree Function

Decimal(1.353) 0.088-1.353



Age Age (years) Numeric(66) 21-66

Diagnosis Is the patient Sick or Healthy?

Varchar(7) Healthy or Sick

Sample Data:



Outlier Detection:



Normalized Data:

Association Rules:



Performance Models:

Decision Tree:



Naïve Bayes:



Neural Network:



SVM:



Logistic Regression:



KNN:



Performance Summary:

Model Name Accuracy



Decision Tree 73.18Naïve Bayes 76.17Neural Network 79.95SVM 77.73Logistic Regression 76.43KNN 100



Evaluation Models:Decision Tree

Naïve Bayes



Neural Net:

SVM

Logistic Regression



K Nearest Neighbor

Evaluation Summary:



Model Name Accuracy (%)Decision tree 71.86Naïve Bayes 75.51Neural Net 74.74Regression 75.65SVM 76.95K Nearest Neighbor 68.24

Answers:

Using the SVM model the above result was generated with a prediction accuracy of 76.95%



Therefore given the medical records of any patient with the above attributes, our model can diagnose the patient for diabetes with a decent accuracy.

References:

Professor’s Class notes YouTube Wikipedia WHO’s dataset


Web Mining Final Report

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