CSE881 project

Diabetes Risk Classification

http://www.cse.msu.edu/~qingpeng/Project/map.html

Jingshu Chen Qingpeng Zhang Ming Wu

CSE@MICHIGAN STATE UNIVERSITY

Don’t let Diabetes get you!

Diabetes• 23.6 million/7.8% of the population in the United States

• estimated 17.9 million have been diagnosed

unfortunately, 5.7 million people (or nearly one quarter) are unaware that they have the disease.

Outline

Overview

Methodology

Experimental Setup

Experimental Evaluation

Visualization Demo

Conclusions & Questions?

Overview

Data Set

Data Mining MethodsGenetic+CFSEval+ J48|JRip|NaiveByesGenetic+consistency+ J48|JRip|NaiveByesRanker+infoGain+ J48|JRip|NaiveByesRanksearch+CFSEval+ J48|JRip|NaiveByesGenetic+classfier+ J48|JRip|NaiveByes

Motivationan effective classification methodimportant featuresBehavior/ indicator --- Diabetes

Related workPima Indian diabetic database (PIDD) at the UCIrvine Machine Learning Lab

The database has n=768 patients each with 8 medical examinations, for an incidence rate of 34.9%.

many studies applying data mining techniques to the PIDD(70%-81%)

C4.5 and it was 71.1%(Quinlan 1993)k-NN for k=9 75.3%(Ripley 1996)neural network (Smith, Everhart et al. 1988)

Method:

Method: data and preprocessing

Select consistent Questions from survey(96-07) (Ming)

Extract records from original data files (Ming) files in ASCII format. fixed record length of about 1000

positions. Extract attributes according to the code book.

Demographic: state, age, gender, height/weight, education income,

employ

Facts: health state, insurance, behavior: diet, exercise, tobacco/alcohol use

medical examination: blood pressure, heart-disorder, drug

Opinions: opinion about AIDS, safety, medical policy…

Challenges for our dataset:• large amount of records:

430,912 records for 2007• Missing value: more than 30% • Not all the attributes are relevant• Most attributes are nominal• One-class classification problem

Method: data mining

Split records according to their states (Jingshu)

before split, each data file have at least 190000 instances.

after split, not only fix out record reduction, but also can get information by areas.

Divide records into training data and test data by random(Jingshu)

Method: data mining

Data mining(Jingshu)

Classifiers: Decision Tree J48, Rule Based JripNaiveBayes

Attribute Selection:

Classifier

Information Gain ranking

Correlation-based Feature Selection(CFS)

Consistency-based Subset Evaluation

Method: Visualization

Visualizationmethod accuracy diagram(Jingshu)Display selected feature by states(Qingpeng)

Google Map APIDiabete Risk Calculate(Qingpeng)Design container to hold all visual results(Qingpeng)

Experimental Setup

Experiments were conducted on Intel(R) core(TM)2 Duo CPU T8100@2.10Ghz, running Windows Vista,4GB RAM

Tools:Weka 3.4.13/Python/Gnuplot

After processing, we have 12year*50states data files, each with 5000more*50attributes

Experimental Evaluation

Classification accuracy with classifier J48

Experimental Evaluation

Classification accuracy with classifier JRip

Experimental Evaluation

Classficiation accuracy with classifier Naïve Bayes

Experimental Evaluation

Average Accuracy(take california for example)

Visualization Demo

Web page: http://www.cse.msu.edu/~qingpeng/Project/index.html

Conclusion

Classification on Diabetes:Consistency+Genetic+ JRip, with average accuracy >97%.

Insights: top features:

Future work:To use selected feature result to do some diabete risk calculator

Demographic: Age, Employ, Sex

Facts: GeneralHealthy, BodyMassIndex, FluShot Exercise, Alcohol, Fruit,

BloodPressure, Heart-disorder

Questions?