Diabetes detection and prediction through a multimodal artificial intelligence framework - Kulkarni, Gururaj N.; Kelapati, Kelapati (Indonesian Journal of Electrical Engineering and Computer Science, 2025)
Diabetes detection and prediction are crucial in modern healthcare, requiring advanced methodologies and comprehensive data analysis. This study aims to review the application of multi-parameters and artificial intelligence (AI) techniques in diabetes assessment, identify existing research limitations and gaps, and propose a novel multimodal framework for enhanced detection and prediction. The research objectives include evaluating current AI methodologies, analyzing multi-parameter integration, and addressing challenges in early detection and model evaluation. The study utilizes a systematic review approach, analyzing recent literature on AI-based diabetes detection and prediction, focusing on diverse data sources and machine learning (ML) techniques. Findings reveal a significant lack of integration of diverse data sources, limited focus on early detection strategies, and challenges in model evaluation. The study concludes with a proposed innovative framework for more accurate and personalized diabetes detection, contributing to the advancement of diabetes research and highlighting the potential of AI-driven healthcare interventions. This research underscores the importance of comprehensive data integration and robust evaluation methods in enhancing diabetes detection and prediction.