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@article{178117,
        author = {Palusa Deepika and Kasoju Shravya and Gurajala Sai Aravind Reddy and Macharla Vivekananda and Mohammed Farooq and Dr. M. Ramesh},
        title = {Medical Assistance and Prediction for Chronic Diseases},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2833-2838},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178117},
        abstract = {Chronic diseases such as diabetes, cardiovascular disorders, and cancer are among the leading causes of death and long-term disability worldwide. These conditions often progress silently, making early detection and intervention essential for effective treatment and improved quality of life. However, traditional diagnostic methods are often time-consuming, reliant on expert interpretation, and reactive in nature. This project proposes a machine learning-based approach to predict the likelihood of chronic diseases by analyzing comprehensive patient data, including demographics, clinical history, lifestyle factors, and genetic indicators. Various supervised learning algorithms, including Decision Trees, Random Forests, Support Vector Machines (SVM), and Neural Networks, are employed to train models capable of making accurate and early predictions. In addition to risk prediction, the system also generates personalized recommendations for lifestyle changes and follow-up actions, aiming to support both healthcare professionals and patients in proactive disease management. Experimental evaluations demonstrate the effectiveness of the proposed model, highlighting its potential to enhance diagnostic accuracy, enable early intervention, and reduce healthcare costs.},
        keywords = {Artificial intelligence, chronic disease prediction, healthcare analytics, machine learning, personalized recommendations},
        month = {May},
        }