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@article{201116,
        author = {Dr. G. Jothi and Dr. D. Rajeswari and Dr. K. Gopinath and Ms. K. Vaanpriya},
        title = {Clinical Decision Support System: Machine Learning Models for Diabetes Diagnosis Using Physiological Parameters},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {7822-7827},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201116},
        abstract = {Diabetes is an ever-growing chronic disease affecting millions of individuals around the world, and the prevalence continues to increase at an alarming rate due to many lifestyle-related factors like diet and lack of exercise. Early diagnosis and management of diabetes is vital in order to prevent complications such as heart disease, kidney failure, and blindness. The traditional methods used to diagnose diabetes rely on the body’s blood sugar detail (BG) and various other markers of the disease. Many of these methods have proven to be inaccurate due to long wait times for testing and several potential points for human error. However, advances in Artificial Intelligence (AI) appear to be improving the accuracy and efficiency of diagnoses regarding diabetes. In this paper reviews several different AI techniques in order to determine their effectiveness in diagnosing diabetes from blood pressure (BP) and BG levels, as well as their prediction ability to identify individuals at risk of developing diabetes and the impact of early identification on the patient’s long-term prognosis. The results indicate that AI algorithms (machines) provide for a more efficient, accurate, and widely accessible method of diagnosing diabetes when BP and BG level data is used collectively.},
        keywords = {Diabetes Detection, Health Analytics, Classification Algorithms, Artificial Intelligence.},
        month = {May},
        }