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@article{178439,
        author = {Rishabh Gautam and Sushant and Tasauvar Ansari and Lav Kumar Dixit},
        title = {Multiple Disease Prediction System Using ML},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5065-5069},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178439},
        abstract = {This study presents a Multiple Disease Prediction System Web Application (MDPSWA) that leverages Machine Learning (ML) and Artificial Intelligence (AI) to enable early and accurate detection of multiple diseases, including diabetes and heart disease. Unlike conventional single-disease prediction models, this system integrates Logistic Regression and Support Vector Machines (SVM) into a unified framework, providing a comprehensive diagnostic tool for healthcare applications. The system utilizes key health parameters such as blood pressure, cholesterol levels, and pulse rate to generate predictions, enhancing preventive care and personalized treatment.
Developed using Python, Scikit-Learn, and Streamlit, the web-based application offers a user-friendly interface for real-time disease risk assessment. The study evaluates the performance of ML algorithms, with SVM achieving 80% accuracy in disease prediction, outperforming other models like Decision Trees (72%) and Linear Regression (80%). Key outcomes include improved diagnostic efficiency, reduced healthcare costs, and high patient satisfaction due to timely and reliable predictions.
The research highlights the potential of AI-driven healthcare solutions in transforming disease management by enabling early intervention, optimizing resource utilization, and improving patient outcomes. Future enhancements aim to expand the system’s capabilities to include cancer and pneumonia prediction, further advancing its applicability in clinical settings. This study underscores the significance of multi-disease prediction systems in modern healthcare, offering a scalable and cost-effective approach to proactive medical diagnostics.},
        keywords = {Machine Learning, Disease Prediction, Healthcare AI, Diabetes, Heart Disease, SVM, Logistic Regression, Streamlit.},
        month = {May},
        }