Comparative Assessment of Machine Learning Algorithms for Effective Diabetes Prediction and Care

The prevalence and impact of diabetes have increased significantly over time, posing a major concern for the healthcare sector globally, especially in India. This study aims to enhance diabetes prediction and management through the use of artificial intelligence (AI) and machine learning (ML) methodologies. We present a range of AI-driven approaches that leverage ML algorithms to classify and predict diabetes more effectively. While most studies utilize the PIMA dataset, a few notable cases have also incorporated custom datasets curated from select healthcare organizations. This research provides a comparative assessment of state-of-the-art diabetes prediction methods alongside carefully selected care strategies. The study is organized into three categories, each exploring distinct approaches, and analyzes methodologies, ML algorithms, accuracy results, and validation metrics. By examining key parameters and techniques, this paper considers diabetes prediction and care tailored to the Indian population, accounting for various influencing factors.