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@article{174284,
        author = {Terli Sai Ram and V.P.V.Bharathi and Vennela Lalam and Mohammed Anish and Ch.Adit Rohan},
        title = {Advanced Health Prognosis Tool},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3347-3351},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174284},
        abstract = {Chronic diseases such as Heart disease, Diabetes, and Parkinson’s disease etc. are leading causes of mortality in India and globally, underscoring the urgent need for accurate diagnostic tools to address these conditions. This study presents a machine learning (ML)-based diagnostic system aimed at enhancing disease prediction accuracy and enabling early intervention. The proposed model analyzes a dataset comprising patient records diagnosed with the diseases, utilizing relevant symptoms to optimize predictions. Employing various machine learning algorithms—including SVM, classification algorithms—our system achieves a high prediction accuracy. The integration of extensive medical data and advanced techniques enables effective analysis for early disease identification, patient care, and community health services. Additionally, the model’s ability to handle both structured and unstructured data enhances its predictive capabilities for the diseases. By leveraging Stream lit, the system facilitates real-time disease prediction, providing healthcare practitioners with valuable insights to improve patient outcomes and reduce mortality rates. A key feature is the integration of an AI-driven chatbot powered by OpenAI, which provides users with an interactive conversational interface. The chatbot assists users by answering health-related inquiries, making it a valuable tool for individuals seeking timely health information and support.},
        keywords = {Prediction, Randomforest, Decision Tree, SVM Classifier, Exploratory Data Analysis, Machine Learning, Deep Learning.},
        month = {March},
        }