DIABETES PREDICTION USING MACHINE LEARNING

Diabetes is a habitual metabolic illness that affects millions of people worldwide. The early discovery and operation of diabetes are critical in precluding complications and enhancing patient issues. Machine learning algorithms have been gradually used to prognosticate and diagnose diabetes, as they can handle large datasets and identify complex patterns in the data that may not be freely identifiable through traditional statistical methods. The survey discusses several machine learning algorithms used for prediction of diabetes, including Decision Tree, Naive Bayes, Random Forest, Support Vector Machine, Artificial Neural Networks, Convolutional Neural Networks, and intermittent Neural Networks. The performance of these algorithms has been estimated on varied datasets, similar as the Pima Indians Diabetes dataset, the National Health and Nutrition Examination Survey dataset, and the University of Virginia Diabetes dataset.