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@article{174559,
        author = {Monesh Kumar and Dr Aakriti Jain and Dr Sitesh kumar Sinha},
        title = {Optimization-Based Mining for Diabetes Prediction: A Comprehensive Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {109-115},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174559},
        abstract = {Diabetes mellitus, a chronic metabolic disorder, poses significant challenges to global public health. Early and accurate prediction of diabetes is crucial for effective management and prevention of complications. This comprehensive review explores the landscape of optimization-based mining techniques in the realm of diabetes prediction. The review begins with an overview of traditional predictive methods, highlighting their limitations and motivating the need for advanced techniques. We delve into the fundamentals of optimization-based mining, elucidating the role of mathematical optimization algorithms in refining predictive models for diabetes. The review surveys various optimization techniques, such as genetic algorithms, particle swarm optimization, and simulated annealing, and their applications in diabetes prediction. Key concepts, methodologies, and challenges associated with optimization-based approaches are systematically examined.
Furthermore, the review scrutinizes studies that have leveraged optimization-based mining in predicting diabetes. It provides a detailed analysis of datasets, features, and performance metrics employed in these studies, offering insights into the effectiveness and limitations of optimization-based models. The hybridization of optimization methods with other predictive techniques is explored, highlighting synergies that contribute to enhanced predictive accuracy. Challenges and limitations inherent in the application of optimization-based mining to diabetes prediction are critically discussed. Factors such as data quality, interpretability, and scalability are examined, paving the way for future research directions. The review concludes with a synthesis of key findings, emphasizing the potential impact of optimization-based mining on advancing the field of diabetes prediction and paving the way for personalized and proactive healthcare interventions},
        keywords = {Machine Learning (ML), Magnetic Resonance Imaging (MRI), Diffusion-weighted imaging (DWI), Support Vector Machines (SVM), Artificial Neural Networks (ANNs), Convolution Neural Networks (CNNs),},
        month = {March},
        }