In-Depth Exploration and Comparison of Machine Learning Performances for Early-Stage Diabetes Risk Prediction

Abstract

Abstract — Diabetes mellitus is distinguished by an inability of the human system to produce insulin on an ongoing basis, as well as by the inefficient utilization of the insulin hormone, resulting in an elevated level of blood glucose. Global diabetes rates have nearly doubled since 1980, reaching 9.3% among adults. Alarmingly, of the 463 million individuals with diabetes, 50.1% are unaware of their condition. Indonesia ranks seventh globally with 10.7 million diabetes cases. In 2019, it was fifth globally for adults (20–79 years) with undiagnosed diabetes. This silent epidemic demands urgent attention and comprehensive strategies for early detection and management. In recent years, researchers have increasingly studied machine learning for early diabetes recognition. In this study, we aim to predict early-stage diabetes risk by utilizing 16 health condition features. We explore 12 distinct machine learning algorithms, applying a hyperparameter grid to tune each algorithm. This involves systematically testing combinations of hyperparameters to identify the optimal settings for achieving the most accurate and reliable predictive models. The results indicate that the Light GBM algorithm achieved the highest accuracy of 0.9692. By contrast, the logistic regression and Naive Bayes algorithms demonstrated the lowest performance, each with an accuracy of 0.8923. The implications of these results underline the capability of employing machine learning algorithms to precisely and effectively detect individuals susceptible to diabetes, enabling the implementation of individualized healthcare approaches.