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@article{196369,
        author = {Mr. P. Chaitanya and T. Hari Gopal and M. Lakshmi Durga Devika and Sk. Ahamad Alisha and T. Renuka Devi},
        title = {Diabetes Data Analysis: Lifestyle and Health Risk Insights},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3280-3284},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196369},
        abstract = {Diabetes is a chronic health condition that affects a large population worldwide and continues to grow at an alarming rate. In recent years, the number of diagnosed cases has increased significantly due to factors such as unhealthy dietary habits, lack of regular physical activity, and hereditary influences. A major concern associated with diabetes is that it often remains undetected in its early stages, as many individuals do not experience noticeable symptoms. As a result, the disease is frequently diagnosed only after it has already caused serious health complications. This highlights the critical need for effective early detection methods that can identify diabetes at an initial stage and help prevent severe damage to the body.
To address this issue, this study proposes a machine learning-based approach for predicting the likelihood of diabetes using medical and lifestyle-related data. Various classification techniques are applied to analyse patient information and identify patterns associated with the disease. The proposed system aims to assist healthcare professionals by providing accurate predictions and supporting early diagnosis. Additionally, the model helps individuals become more aware of their health condition and take preventive measures at the right time. Experimental results demonstrate that the developed model achieves reliable performance in terms of accuracy and prediction capability. Overall, this work contributes to the development of intelligent healthcare systems that can improve early detection and reduce the impact of diabetes on human life.},
        keywords = {Blood Pressure, BMI, data preprocessing, decision tree, diabetes prediction, exploratory data analysis, glucose, health risk, lifestyle, logistic regression, machine learning, PIMA dataset, feature importance, classification, chrnic disease},
        month = {April},
        }