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@article{179339,
        author = {Mohit Aggarwal and Manish Yadav and Shubham Yadav and Arshad Khan},
        title = {Health Prognosis System Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6528-6534},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179339},
        abstract = {This research paper explores the development of a Health Prognosis System (HPS) leveraging machine learning (ML) algorithms to predict multiple diseases, such as diabetes, heart disease, and Parkinson’s disease. The system integrates logistic regression and support vector machines (SVM) within a user-friendly interface powered by Streamlit. Key objectives include enhancing early detection, improving personalized healthcare, and addressing limitations in existing single-disease prediction models. By employing various datasets, preprocessing techniques, and performance metrics, this paper highlights the efficacy of ML algorithms in providing reliable health prognoses.},
        keywords = {Machine Learning, Health Prognosis System, Streamlit, Logistic Regression, Support Vector Machines, Multi-Disease Prediction.},
        month = {May},
        }