A Deep Learning Enabled Chatbot Approach for Self-Diagnosis

Abstract: Healthcare is an essential need that is often inaccessible or unavailable. With rising populations and deadly viruses, the load on healthcare workers is the highest, while the ratio of healthcare workers per person has hit an all time low. This extreme demand for assistance in the face of unavailability has opened up avenues for computer assisted healthcare. In this paper, we propose an approach to use advancements in computer assisted healthcare in two directions. We employ machine learning algorithms to predict the disease correctly. We used Random Forest and XGBoost as this system's core classifiers that gave an accuracy of 100% and 99.0% correspondingly. The chatbot is used to trigger the appropriate function for the disease prediction concluding from the conversation with the user. It has an accuracy of 97.22%. Overall our chatbot has the potential to provide immediate assistance to those who are in need, and in turn, reduce the burden on healthcare workers.