Computation of an Enriched Set of Predictors for Type 2 Diabetes Prediction

According to World Health Organization, about 422 million people worldwide have diabetes, vast majority of whom belong to Type 2. In addition to this population, a noticeable percentage of people has either undiagnosed Type 2 diabetes or prediabetes. Since this disease causes death mainly through physiological complications such as cardiovascular disease, it is highly crucial to diagnose it in an early stage. The medical diagnosis is done by three invasive blood tests which make it almost impossible to periodically screen the whole population. As an alternative approach, development of automated systems that can identify patients having Type 2 diabetes using non-invasive predictors such as age, waist circumference, family history and body mass index is extensively studied. In this thesis, the use of an enriched set of predictors including symptoms, diagnoses, lifestyle habits and medications is considered for improving the detection performance. The main motivation for this study is that the complications due to the onset of the disease might occur before medical diagnosis. The performance of various classifiers including logistic regression and support vector machines, and feature selection schemes such as mRMR and Relief are investigated. The experiments conducted have shown that additionally defined features provide better area under the receiver operating characteristic curve scores. Keywords: Type 2 Diabetes Classification, Feature Extraction, Feature Selection, Filters, Wrappers, Embedded Feature Selection