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@article{175360,
        author = {Manali Sutariya and Rauky Yadav},
        title = {A Multi-Disease Detection System Using Machine Learning: A Case Study on Diabetes, Parkinson’s, and Heart},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2454-2458},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175360},
        abstract = {In recent years, the integration of Machine Learning (ML) techniques in the healthcare sector has significantly enhanced the accuracy and efficiency of disease diagnosis. This paper presents a unified ML-based system designed to detect three major chronic diseases: Diabetes, Parkinson’s Disease, and Heart Disease. Utilizing publicly available datasets from trusted repositories, the system applies a comparative analysis of various ML algorithms including Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forest. The models were trained and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that ensemble-based approaches like Random Forest consistently yield higher predictive performance across all datasets. The proposed system aims to assist medical professionals in early diagnosis and decision-making, ultimately improving patient outcomes. Future enhancements may include deep learning integration and real-time prediction through web or mobile deployment.},
        keywords = {},
        month = {April},
        }