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@article{175066,
        author = {Sonali Kumari and Sadhana Mitra and Sony jena and Sriramdasu Nagendrachary},
        title = {Disease Prediction and Health Guidance Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2350-2356},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175066},
        abstract = {The Disease Prediction and Health Guidance System is a machine learning-based application designed to predict potential diseases based on user-inputted symptoms. It utilizes a structured dataset of symptoms and corresponding diseases for model training. The previous system employed Decision Tree, Random Forest, and Naïve Bayes classifiers, while the current system uses K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), improving classification accuracy. Additionally, the system provides users with personalized health recommendations, including medication, workouts, and dietary guidance. The frontend is developed using HTML, CSS, JavaScript, and Bootstrap, while Python with Flask powers the backend. By integrating machine learning-driven predictions, this system enhances accessibility to preliminary medical guidance, reducing unnecessary doctor visits and promoting early detection of health conditions. The previous system achieved an accuracy of 85.6%, whereas the current system has improved to 91.2%.},
        keywords = {},
        month = {April},
        }