AI-DRIVEN CLINICAL DECISION SUPPORT SYSTEM FOR INTELLIGENT MEDICAL REPORT ANALYSIS AND DISEASE PREDICTION

Abstract: The AI-Driven Clinical Decision Support System for Intelligent Medical Report Analysis and Disease Prediction enhances healthcare decision-making by integrating Artificial Intelligence into clinical data processing. The system uses Natural Language Processing (NLP) to extract symptoms from patient complaints, suggests relevant medical tests, and applies machine learning algorithms to analyze laboratory reports and detect abnormal values. It predicts possible heart, liver, and kidney diseases with probability scores, identifies the key factors behind each prediction, and generates structured clinical summaries to assist doctors in faster evaluation. The heart disease module is built on the merged UCI Heart Disease dataset containing 920 records and 14 attributes. The prototype, developed in Python with spaCy, scikit-learn, SQLite, and Streamlit, demonstrates the complete workflow from patient registration to an explained prediction. Designed as a decision-support tool, the system reduces diagnostic workload, improves patient prioritization, and supports timely intervention while ensuring that final medical decisions remain under professional supervision.

Keywords: Clinical decision support system, electronic health records, natural language processing, machine learning, disease prediction, explainable AI, SHAP, clinical summary.