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@article{191006,
        author = {Shaikh Ifra Ahmed and Kulkarni Aarti  Balasaheb and Prof.O.M.Patil and Prof.Sunil M Kale},
        title = {Multi-Disease Prediction System Using Machine Learning and Flutter},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {4738-4743},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191006},
        abstract = {Early prediction of chronic diseases can significantly reduce complications by enabling timely medical intervention. This paper presents the design and implementation of a Multi Disease Prediction System for assessing the risk of diabetes, heart disease, liver disease, and kidney disease. Separate Logistic Regression models are trained on publicly available medical datasets using Python, Pandas, NumPy, and scikit learn. Each prediction task is formulated as a binary classification problem (disease / no disease). The trained models are deployed through a lightweight Flask/FastAPI backend and accessed by a Flutter based desktop application running on Windows. Users enter basic clinical parameters such as age, blood pressure, glucose level, cholesterol, and creatinine values, and receive near real time prediction results in an intuitive interface. Experimental evaluation on the respective test datasets shows that the models achieve satisfactory accuracy and provide reliable preliminary risk assessment. While the system is not intended to replace professional medical diagnosis, it can serve as an effective early warning and awareness tool, encouraging users to seek timely consultation when a higher risk is indicated.
 The results demonstrate that Logistic Regression, despite its simplicity, provides reliable performance for preliminary risk assessment. The developed system is not intended to replace clinical diagnosis, but to act as an early warning and awareness tool to encourage users to seek professional medical advice.},
        keywords = {Disease Prediction, Logistic Regression, Machine Learning, Flutter, e-health, Desktop Application, Heart Disease, Liver Disease, Kidney Disease.},
        month = {January},
        }