Applications of Machine Learning for Diabetes Prediction

Abstract

The use of machine learning techniques has drawn more attention due to its potential to improve early identification and intervention in diabetes, a critical global health concern. This article offers an extensive overview of the various machine learning algorithms used in diabetes prediction, including ensemble techniques, logistic regression, support vector machines, decision trees, and neural networks. The research closely examines how these algorithms make use of a variety of data sources, including wearable sensor data, electronic health records, clinical data, and genetic information. The report also emphasizes the difficulties that these applications face, including as interpretability, model integration into clinical procedures, and ethical issues. This review elucidates the significant influence of machine learning on diabetes prediction, paving the way for more useful risk assessment, individualized therapies, and improved patient outcomes. It does this by thoroughly examining recent studies and their conclusions.