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@article{178131,
        author = {Chaithrashree M C and Lekhana DS and Likhitha N and Dr. Prakash A},
        title = {Multiple Disease Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2873-2875},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178131},
        abstract = {This study presents a multiple disease prediction system utilizing machine learning techniques to assess the likelihood of diabetes, heart disease, and Parkinson's disease. With the rising prevalence of these chronic conditions, early detection and intervention are crucial for improving health outcomes. The application features a user-friendly web interface developed with Streamlit, allowing users to input relevant health parameters for real-time predictions. Models were trained on established datasets, including the PIMA Diabetes dataset and a heart disease dataset, employing algorithms such as Support Vector Machine (SVM) and Logistic Regression, achieving accuracy scores of approximately 78% and 85%, respectively. Additionally, a model for Parkinson's disease prediction was developed using vocal features. This system enhances accessibility to health information and empowers individuals to take proactive steps in managing their health. The findings highlight the potential of machine learning in healthcare, offering a scalable solution for disease prediction and management.},
        keywords = {Machine Learning; Support Vector Machine; K-Nearest-Neighbor; Random Forest; Diabetes; Heart disease; Parkinson's disease;},
        month = {May},
        }