TEJAS Journal of Technologies and Humanitarian Science

Saumya Rai
Scholar, Department of Computer Science & Engineering, KIPM College of Engineering and Technology, U.P., India

Author

Dr. R K Singh
Assistant Professor, Department of Computer Science & Engineering, KIPM College of Engineering and Technology, U.P., India

Author

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Abstract:

Proposed paper on an AI-Driven Health Diagnostic and Disease Prediction System that analyzes patient symptoms, medical history, and clinical metrics. Multiple machine learning models, including XGBoost, Random Forest, and LightGBM, were trained and evaluated using accuracy, precision, recall, and F1-score. XGBoost delivered the best predictive performance, accurately identifying high-risk patients while reducing false diagnoses. The system provides a scalable framework for integrating machine learning into healthcare platforms, enabling early detection, faster diagnosis, and data-driven clinical decision support. This approach improves patient outcomes, reduces diagnostic delays, and strengthens overall healthcare efficiency.

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