Prognostic Biomarkers Identification for Diabetes Prediction by Utilizing Machine Learning Classifiers

Abstract

Diabetes caused 4.2 million deaths in 2019 alone which makes it the seventh leading cause of death worldwide. Although diabetes can be treated, late treatment can be fatal and may result in early death. Moreover, diabetes is a costly disease to maintain, hence, early detection of diabetes can facilitate the patients by indicating the time to seek treatment and to get prepared mentally and financially. Previously, various studies suggested and proposed different approaches for achieving near-perfect accuracy but not many works focused on finding the appropriate attributes which can predict the disease at the early stage. In this study, we focused on finding those significant features and our experimental analysis showed the findings of 10 significant features that can achieve a near-perfect recognition of 98.08%. The feature selection approaches used in this research are the Chi-Square test, the Minimum Redundancy Maximum Relevance (mRMR) test, and the Recursive Feature Elimination test based on Random Forest (RFE-RF). Also, the seven classifiers utilized in this research are Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM).