A Comprehensive Review on the Prediction of Diabetes Disease Using Machine Learning

In this review, we delve into the use of machine learning techniques for predicting diabetes. The paperdiscusses various applications of these techniques and provides insights into their effectiveness. Prior prediction and management of diabetes can reduce the burden on healthcare systems and significantly improve patient outcomes. Machine learning models have demonstratedsignificant potential in analyzing diverse datasets, incorporating various risk factors, and providing accurate predictions. This delivers an overview of the various ML algorithms and ways employed for diabetes prediction. It discusses the selection of relevant features, data preprocessing techniques, and evaluation metrics commonly used in the context of diabetes prediction. Additionally, the paper examines the challenges and limitations associated with ML- based diabetes prediction, including issues related to data quality, interpretability, and generalization. Furthermore, the review highlights prominent studies and datasets in the field, showcasing the performance and comparative analyses of different ML models. By critically analyzing the existing literature, this paper aims to offer insights into the current state of diabetes prediction using ML and identify potential avenues for future research.