Accurate AI-Based Chatbot to Diagnose Heart Diseases Pre-Human Doctor Consultation

Abstract

Early diagnosis of heart disorders is an essential issue since they might be life-threatening conditions that call for urgent medical care. Chatbots as a user-friendly technology can be used to deliver general assistance and information anywhere anytime. However, the decision-making accuracy is one of the key challenges of Chatbots. In this paper, a medical Chatbot is developed for the purpose of early cardiac disease diagnosis based on given symptoms. An approved dataset known as the Cleveland Heart Disease dataset is utilized in this work. Hence, for accurate decision-making, three machine learning algorithms Support Vector Machine (SVM), and Logistic Regression (LR), and Extreme Gradient Boosting (XGBoost) are compared and tested to nominate the best algorithms for heart health disorders detection. Three simulation scenarios are applied to test the performance of each algorithm using first all features of the dataset, then the k-best feature selection method, and the application of Grid Search (GS) optimization algorithms. The performance evaluation shows that the prediction accuracy of the proposed XGBoost algorithm outweighs other algorithms.