Explainable AI-Driven Chatbot System for Heart Disease Prediction Using Machine Learning
Salman Muneer, Taher M. Ghazal, Tahir Alyas, Muhammad Ahsan Raza, Sagheer Abbas, Omar H. AL‐Zoubi, Oualid Ali
Abstract
Heart disease (HD) continues to rank as the top cause of morbidity and mortality worldwide, prompting the enormous importance of correct prediction for effective intervention and prevention strategies. The proposed research involves developing a novel explainable AI (XAI)-driven chatbot system for HD prediction, combined with cutting-edge machine learning (ML) algorithms and advanced XAI techniques. This research work highlights different approaches like Random Forest (RF), Decision Tree (DT), and Bagging-Quantum Support Vector Classifier (QSVC). The RF approach achieves the best performance, with 92.00% accuracy, 91.97% sensitivity, 56.81% specificity, 8.00% miss rate, and 99.93% precision compared to other approaches. SHAP and LIME provide XAI methods for which the chatbot's predictions and explanations endow trust and understanding with the user. This novel approach proves the potential of seamless integration of explanations in a wide range of web or mobile applications for healthcare. Future works will extend the work on incorporating other diseases' predictions in the model and improve the explanation of those predictions using more advanced explainable AI approaches.