Computational Approaches to Diabetes Risk Assessment: A Review of Data-Driven Techniques

Over 540 million people worldwide suffer from diabetes mellitus, making it a serious global health concern. The advancement of robust predictive models that surpass traditional risk assessment approaches has demonstrated significant potential due to machine learning techniques. This thorough analysis summarizes the state of the art in machine learning-based diabetes prediction systems by examining algorithmic approaches, dataset properties, and performance indicators. The analysis shows how advanced ensemble and deep learning techniques have replaced more conventional statistical methods in order to achieve better results. Critical drawbacks still exist, nonetheless, such as an excessive dependence on datasets with a restricted demographic, a lack of real-world validation, and inadequate model interpretability for clinical acceptability. Regulatory obstacles, population-specific dataset variability, and discrepancies between algorithmic performance and therapeutic impact are some of the main obstacles. In order to convert advancements into clinically useful systems, future priorities include creating representative datasets, putting explainable artificial intelligence (AI) into practice, and carrying out prospective clinical studies.