Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{202313,
        author = {Mayur Sanjay Bari and Om Deepak Tapkire and Mohit Pramod Narkhede and Prof. Pooja Nitin Chaudhari},
        title = {A Machine Learning Framework for Multi-Disease Prediction from Clinical Data},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {8055-8059},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202313},
        abstract = {Early and accurate diagnosis of chronic diseases is a critical challenge in modern healthcare. This paper presents a Machine Learning Framework for Multi-Disease Prediction from Clinical Data a unified, web-based system capable of simultaneously predicting Diabetes, Heart Disease, and Parkinson's Disease using supervised machine learning models. The proposed system leverages pre-trained classification algorithms including Support Vector Machines, Random Forest, and Logistic Regression trained on established medical datasets. Deployed through an interactive Streamlit interface, the system enables healthcare professionals to input patient-specific clinical parameters and receive immediate binary predictions. The framework follows a modular architecture with distinct components for model loading, input validation, feature extraction, and result visualization. Experimental evaluation demonstrates clinically relevant accuracy across all three disease modules: approximately 78–82% for diabetes, 85–88% for heart disease, and 90–93% for Parkinson's detection. The system prioritizes local data processing to ensure patient privacy and is designed for extensibility, supporting future integration of additional disease modules. This work demonstrates the feasibility of integrated multi-disease prediction as a practical clinical decision support tool.},
        keywords = {Machine Learning, Disease Prediction, Diabetes, Heart Disease, Parkinson's Disease, Streamlit, Clinical Decision Support, Random Forest, Support Vector Machine, Healthcare Analytics},
        month = {May},
        }