An Extensive Examination of Machine Learning Methods for Identifying Diabetes

Abstract

This paper presents a comprehensive examination of machine learning (ML) methods employed in the identification and management of diabetes. With the rising prevalence of diabetes globally, there is a pressing need for accurate and efficient diagnostic tools. ML techniques offer promising avenues for enhancing diagnostic accuracy, risk prediction, and personalized treatment approaches. This study reviews a range of ML algorithms applied to various datasets for diabetes detection, including supervised, unsupervised, and hybrid approaches. The examination of each algorithm's performance aims to identify the one displaying superior accuracy, precision, sensitivity, and specificity. Additionally, the decision-making process is evaluated to enhance the model. Through rigorous evaluation and comparison, insights are drawn regarding the effectiveness and applicability of each model in the context of diabetes prediction. The findings contribute to advancing the understanding of machine learning methodologies in healthcare and offer valuable guidance for developing robust predictive models for diabetes diagnosis and management. The study aims to contribute valuable insights into the application of machine learning techniques for diabetes prediction.