Early-stage diagnosis of diabetes mellitus using machine learning and uncertainty quantification

Abstract

Diabetes is a chronic condition that has the power to ruin world health. A total of 3820 million people worldwide have diabetes, and the International Diabetes Federation (IDF) projects that number to double over the next 15◦years. Increase in blood glucose levels is a defining feature of diabetes, commonly known as diabetes mellitus. This condition can be determined using a variety of physical and chemical testing. The eyes, heart, kidneys, feet, and nerves are just a few of the human body parts that can be harmed by uncontrolled and incorrectly diagnosed diabetes, in addition to death. Thus, detecting and analyzing diabetes early can reduce the mortality rate. The research aims to develop a machine learning model for accurately predicting diabetes in humans using classifiers like Support Vector Machine (SVM), K-Nearest Neighbors (KNN), logistic regression, Navie Bayes, Gradient Boosting, Decision Tree, Random Forest, and Ensemble Learning. The study uses the Pima Indian Diabetes Database (PIDD) dataset from Kaggle. Performance is compared using accuracy scores, Receiver Operating Characteristic (ROC), F-measure, and L1-Loss function. Uncertainty in medical datasets is addressed