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@article{191230,
        author = {K. Savithiri and S. Shirley},
        title = {SMART DISEASE PREDICTION AND DOCTOR RECOMMENDATION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {5818-5822},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191230},
        abstract = {Early identification of diseases and timely consultation with appropriate medical professionals are critical for improving healthcare outcomes. This paper presents a web-based Disease Prediction and Doctor Recommendation System that assists users by predicting possible diseases based on symptoms and recommending suitable doctors for further consultation. The system accepts symptom descriptions in textual form and applies Natural Language Processing techniques to preprocess and transform unstructured input into numerical features. An XGBoost classification algorithm is employed to predict diseases due to its high accuracy and efficiency. Based on the predicted disease, the system recommends doctors according to medical specialization. The application is implemented using Python and the Flask web framework to provide real-time interaction through a user-friendly interface. Experimental observations indicate that the system delivers reliable predictions with minimal response time. The proposed solution demonstrates the effective use of machine learning and NLP in developing scalable and cost-effective healthcare decision-support systems},
        keywords = {Disease Prediction, Doctor Recommendation, Machine Learning, Natural Language Processing, XGBoost, Flask, Healthcare Decision Support System},
        month = {January},
        }