An In-Depth Exploration of Machine Learning Algorithms and Performance Evaluation Approaches for Personalized Diabetes Prediction
Inderdeep Kaur, Aleem Ali
Abstract
Diabetes is now recognised as a major worldwide health problem, demanding effective early detection and prediction measures to limit its repercussions. Machine learning algorithms have shown to be excellent tools in healthcare for creating prediction models. This paper delves into an extensive investigation of machine learning (ML) algorithms and methodologies for assessing their performance in the context of personalized diabetes prediction. Different machine learning models are rigorously explored and evaluated for how well they can customise forecasts to the unique characteristics of individual patients. The study also critically assesses several performance evaluation techniques in an effort to improve the accuracy and reliability of diabetes prediction algorithms. Furthermore, interpretability problems and class imbalance are addressed as challenges and limitations to the application of machine learning in the prediction of diabetes. This study aims to motivate researchers, facilitating a deeper understanding of disease prediction algorithms and conduct related research.