Prediction of risk factors for pre-diabetes using a frequent pattern-based outlier detection

Abstract

Pre-diabetes is the forerunner stage of diabetes. Pre-diabetes develops type-2 diabetes slowly without any predominant symptoms. Hence, pre-diabetes has to be predicted apriori to stay healthier. The risk factors for pre-diabetes are abnormal in nature and are found to be present in a few negative test samples (without diabetes) of Pima Indian Diabetes data. The conventional classifiers will not be able to spot these abnormal samples among the negative samples as a separate group. Hence, we propose algorithm frequent pattern-based outlier detection (FPBOD) to spot such abnormal samples (outliers) as a separate group. FPBOD uses an associative classification technique with few surprising measures like lift, leverage and dependency degree to detect outliers. Among which, lift measure detects more precise outliers that are able to correctly classify the person who did not have diabetes, but just takes the risky chance of being a diabetic patient.