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@article{182966,
        author = {Rashmi G S and Prof. Naseema C A and Dr. Bhagyajyothi K L and Prof. Tajunnisa N M},
        title = {Lifestyle Disease Prediction and Recommendation Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {3871-3875},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182966},
        abstract = {Poor dietary habits, sedentary lifestyle, and lack of physical activity often lead to lifestyle diseases such as diabetes, hypertension, and obesity. Early detection is vital to minimize their health impact and reduce strain on healthcare systems. This study presents a Lifestyle Disease Prediction and Recommendation System that analyzes individual health and lifestyle attributes to assess risk and offer personalized guidance using machine learning.
Four supervised algorithms—Support Vector Machine (SVM), Logistic Regression, Random Forest, and K-Nearest Neighbors (KNN)—are used to classify individuals into low, medium, or high-risk categories. A structured preprocessing pipeline ensures data quality through cleaning, encoding, and feature scaling. Model performance is evaluated using metrics like accuracy, precision, recall, F1-score, and confusion matrix.
A lightweight, rule-based recommendation module provides lifestyle suggestions based on parameters like BMI, blood pressure, and blood sugar. By integrating prediction and guidance, this system highlights the role of machine learning in preventive healthcare and supports the development of accessible, user-friendly tools for proactive health management.},
        keywords = {Disease Risk Prediction, Lifestyle Diseases, Personalized Recommendations, Preventive Healthcare, Supervised Learning Algorithms},
        month = {July},
        }