Machine Learning Methods for Diabetes Prediction: A Literature Review Paper

Abstract

Diabetes mellitus is a long-lasting medical condition stemming from either insulin resistance or insufficient insulin production. Insulin is responsible to control level of glucose in blood. Hyperglycemia, alternatively known as raised blood glucose or raised blood sugar, frequently results from uncontrolled diabetes and can lead to significant harm to various bodily systems, particularly the nerves and blood vessels, when left untreated over time. Early diagnosis and prediction of diabetes can significantly improve patient health. Machine learning methods have become valuable tool for predicting diabetes, and their capacity to analyze extensive datasets and uncover complex patterns. The exploration of machine learning methods aims to uncover suitable strategies for efficiently classifying the diabetes dataset and extracting valuable patterns. Over the years, numerous researchers have endeavored to develop precise diabetes prediction models. This comprehensive review aims to encapsulate the diverse array of methodologies, advancements, and findings in the field, shedding light on the intricacies associated with predicting diabetes through the lens of modern methods of machine learning