DocBot: A System for Disease Detection and Specialized Doctor Recommendation Using Patient’s Speech of Symptoms

Nowadays, Machine Learning (ML) plays a crucial role in improving healthcare by enabling researchers, doctors, and patients to explore, diagnose, and prevent diseases such as dengue, typhoid, jaundice, pneumonia, and other major ailments. Our research focuses on leveraging ML to detect various diseases from a patient’s speech. The patient will describe their symptoms to the machine, akin to explaining their concerns to a doctor. The machine will then identify the disease and provide primary medication recommendations along with suggesting a specialized doctor for that particular ailment. To optimize our system’s performance, we trained our machine using multiple algorithms and evaluated their results. Our evaluation revealed an accuracy of 86.59% for Naive Bayes, 83.17% for Unhyperd SVM, 98.05% for Hyperd SVM, 97.4% for Decision Tree, and the highest accuracy of 99.35% was achieved by Random Forest.