Critical Factor Analysis for prediction of Diabetes Mellitus using an Inclusive Feature Selection Strategy

Abstract

Diabetes mellitus is a metabolic disorder that significantly implicates serious consequences in various parts of the human body, such as the Eye, Heart, kidney, Nerves, Foot, etc. The identification of consistent features significantly helps us to assess their impact on various organs of the human body and prevent further damage when detected at an early stage. The selection of appropriate features in the data set has potential benefits such as accuracy, minimizing complexity in terms of storage, computation, and positive decision-making. The left features might contain potential information that would be useful for analysis. In order to do effective analysis, additionally, all features should be studied and analyzed in plausible ways, such as using more feature selection (FS) methods with and without standardization. This article focuses on analyzing the critical factors of diabetes by using univariate, wrapper, and brute force FS techniques. To identify critical features, we used info gain, chi-square, RFE, and correlation using the NIDDK data. Later, distinct machine learning models were applied to both phases of the feature sets. This study was carried out in two phases to evaluate the efficacy of the techniques employed. The performance has been assessed using accuracy, F1score, and recall metrics.