A Comprehensive Survey of Intelligent Approaches for Diabetes Diagnosis and Management

Abstract

This study offers a thorough review and comparative analysis of machine learning techniques for managing diabetes, with emphasis on forecasting blood sugar levels and the need for insulin therapy. This research encompasses a broad spectrum of methodologies, such as conventional classification models, support vector machines (SVMs), and sophisticated deep learning algorithms such as ARCHANO. Important discoveries show that, in comparison to broad datasets, personalized datasets produce better predictive accuracy for diabetes management. Furthermore, the use of support vector machines to forecast blood glucose levels in individuals with insulin-dependent diabetes mellitus is promising, and rigorous frameworks for prediction accuracy in blood glucose control have been provided by causality modelling and fuzzy lattice reasoning. Neural network models demonstrate tremendous potential for optimizing insulin dosage prediction in patients. Additionally, studies utilizing decision tree algorithms and association rule mining for diabetes diagnosis and treatment have examined the dynamic interactions between different clinical factors. More accurate therapeutic interventions are made possible by time series temporal mining and stream mining classifiers, which improve real-time decision support systems. It is also investigated how hybrid algorithms, including those that combine particle swarm optimisation and fuzzy temporal rules, may be able to improve the management of diabetes. The analysis comes to the conclusion that more accurate and individualised treatment strategies for diabetic patients can result from incorporating these cutting-edge machine learning and data mining approaches into clinical decision support systems (CDSS). Together, the various strategies show how predictive analytics can be used to enhance the quality of diabetes care.