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{206083,
        author = {Shridhar Behera and Aakanksha Sahu and Adarsh Kumar Soni},
        title = {Machine Learning Approaches for Multiple Disease Prediction: A Comprehensive Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {158-163},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206083},
        abstract = {Chronic and non-communicable diseases such as diabetes mellitus, cardiovascular disease, and Parkinson’s disease continue to impose a substantial burden on global healthcare systems, making early and accurate diagnosis a pressing clinical priority. Over the past decade, machine learning (ML) has emerged as a powerful tool for building data-driven clinical decision support systems capable of predicting disease risk from structured patient records and biomedical signals. This review synthesizes findings from fifteen peer-reviewed studies published between 2007 and 2025 that apply supervised learning algorithms—including Support Vector Machines, Logistic Regression, Random Forest, K-Nearest Neighbour, Naive Bayes, Decision Trees, ensemble methods, and deep learning models—to disease prediction tasks. The reviewed literature is organized around three disease domains and a fourth, emerging category of unified multi-disease prediction frameworks. A comparative analysis of algorithmic performance, dataset characteristics, and methodological choices is presented in tabular form. The review further identifies recurring research gaps, including limited dataset diversity, weak external validation, inconsistent preprocessing pipelines, and the scarcity of genuinely integrated multi-disease platforms. Finally, promising directions for future research are outlined, spanning federated learning, explainable artificial intelligence, multimodal data fusion, and real-time clinical deployment.},
        keywords = {Machine Learning, Disease Prediction, Diabetes, Heart Disease, Parkinson’s Disease, Classification Algorithms, Healthcare Informatics, Computer-Aided Diagnosis.},
        month = {July},
        }