DiagnoBot: A Medical Chatbot

Abstract

Many people live without access to healthcare or delay care due to inconvenience, work, cost, living in rural areas, or social/medical fears (Gertz, Pollack, Schultheiss, & Brownstein, 2022), (Golembiewski, et al., 2022). Medical chatbots have emanated as a potential solution to healthcare access and to promote self-care. Our goal is to provide medical information through conversation to those who may otherwise delay seeking care. A Rasa chatbot is created using our Disease Prediction System, which utilizes machine learning algorithms i.e., Decision Trees, Gradient Boosting, Support Vector Machine (SVM), and Naïve Bayes to guide users to a sensible diagnosis, so they may opt to self-care at home or seek medical attention. In this paper, a sample of 4920 patient records with 41 disorders is analyzed. A Recursive Feature Elimination algorithm is used to enhance 95 out of the 132 symptom features. Our system achieved 97-100 percent accuracy.