Diabetes Detection Using Machine Learning and Deep Learning Approaches

The increasing prevalence of diabetes worldwide has prompted the medical community to explore advanced

technologies for efficient and intelligent detection systems. Machine learning and deep learning methodologies

have emerged as promising tools in this domain. This study critically examines contemporary advancements in

these techniques for diabetes identification and classification. A significant challenge identified is the limited

availability of comprehensive diabetes dataset.This study aims to bridge the gap in effective diabetes

management by employing advanced machine learning (ML) and deep learning (DL) technologies. By

automating the detection process, we not only improve the accuracy and efficiency of diabetes diagnosis but

also pave the way for non-invasive monitoring solutions. The integration of these technologies holds immense

promise for early intervention, ultimately reducing diabetes complications and healthcare costs. Furthermore, the potential of real-time analysis and remote monitoring could significantly enhance patient care outcomes globally