Predictive Analytics in Healthcare for Diabetes Prediction: Leveraging Machine Learning for Early Detection and Intervention

Abstract

Stemming from Type 2 diabetes, the prevalence of this condition has turned into a leading worldwide health crisis with increased population-based exhibition rates. The early identification of disease followed by proactive intervention remains essential for avoiding both diabetic disease development and its linked medical problems. The analysis of patient information by predictive analytics with artificial intelligence (AI) algorithms and machine learning (ML) shows great potential for diabetes early detection and prediction of at-risk patients. This research investigates diabetes prediction within healthcare settings using predictive analytics by examining three machine learning models which combine decision trees with support vector machines and deep learning algorithms to study patient datasets for predicting diabetes risk. Our discussion investigates how these methods enhance early diabetes detection while creating custom treatment strategies and enhance healthcare resource execution. Our study examines data quality issues along with privacy concerns and model interpretability problems during the clinical implementation of predictive analytics systems for diabetes management.