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@article{201011,
        author = {P. Ganesh and D. Sandhiya and V. Rakshantha and R. Kaviya},
        title = {Machine Learning-Based Multi-Disease Prediction with Personalized Health Recommendation System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {150-156},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201011},
        abstract = {The escalating burden of chronic and lifestyle-related diseases globally necessitates the development of intelligent, automated diagnostic tools that can assist healthcare professionals and patients in early disease detection. This paper presents a comprehensive machine learning-based multi-disease prediction system integrated with a personalized recommendation engine, deployed as an interactive web application using Streamlit. The proposed system simultaneously predicts multiple diseases, including Diabetes, Heart Disease, Parkinson's Disease, Breast Cancer, and kidney disease, using a suite of supervised machine learning algorithms such as Support Vector Machine (SVM), Random Forest, Logistic Regression, K-Nearest Neighbors (KNN), and Decision Trees. Each disease prediction module is trained on benchmark datasets sourced from the UCI Machine Learning Repository and Kaggle. Beyond prediction, the system incorporates a rule-based and content-filtering recommendation engine that provides personalized dietary advice, lifestyle modifications, and medical consultation suggestions based on the prediction outcome. Experimental results demonstrate that the proposed system achieves high classification accuracy across all disease modules, with the SVM-based heart disease predictor achieving 85.2%, the Random Forest diabetes predictor achieving 87.4%, and the Parkinson's disease predictor achieving 94.8% accuracy respectively. The Streamlit-based web interface ensures accessibility to non-technical users, while the modular architecture allows easy extension to additional diseases. This system has the potential to serve as a cost-effective, scalable, and intelligent clinical decision support tool.},
        keywords = {Machine Learning, Multi-Disease Prediction, Recommendation System, Streamlit, Support Vector Machine, Random Forest, Clinical Decision Support, Healthcare AI, Diabetes, Heart Disease, Parkinson's Disease.},
        month = {May},
        }