<![CDATA[Recent research in health data analysis has transformed our understanding, prediction, and management of diabetes mellitus. This review explores various approaches used in related studies to enhance understanding and management strategies of diabetes through data analysis. Various data analysis methods, including machine learning such as neural networks, Gaussian Process Classification (GPC), and deep learning, have been used to enhance illness management and forecast accuracy. One of the included studies created customised care plans and used data to forecast the likelihood of complications in diabetes.. Another focused on comparative approaches for diabetes diagnosis using artificial intelligence, while others explored disease classification techniques using GPC algorithms. On the other hand, some studies utilized deep learning to identify diverse trajectories of type 2 diabetes from routine medical records, while others developed wide and deep learning models to predict diabetes onset. This review notes that data analysis approaches have significantly advanced accuracy in diagnosis, predictive modeling, and disease management of diabetes. Integrating these technologies allows for more personalized treatment approaches, where patient data can tailor individualized care strategies. Study findings indicate that machine learning and deep learning applications not only enhance prediction accuracy but also unlock new potentials in identifying risk factors, managing complications, and preventing diseases. Thus, this review provides profound insights into how data analysis has shifted paradigms in diabetes management, extending beyond diagnosis and treatment to encompass prevention and long-term management of chronic diseases. These studies lay a robust foundation for further research in developing more sophisticated and effective approaches in health data analysis, ultimately aiming to enhance the overall quality of life for patients with diabetes.]]>
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