A Medical Chatbot for Tunisian Dialect using a Rule-Based and Machine Learning Approach

People nowadays have hectic schedules, so they tend to neglect their health because traveling to and from a hospital takes a significant amount of time. Many people would rather buy medications from a pharmacy than see a doctor. As a result, in terms of time, cost, and convenience, interacting with chatbots to obtain useful medical information could be a viable solution for them to overcome the aforementioned issues. In this paper, we propose a method to build a medical chatbot for the Arabic language and more specifically the Tunisian Dialect (TD). For that we collected, from patients, a set of 356 pairs of question/responses in TD. A variety of heterogeneous methods developed from standard machine learning were trained and a combination between Machine Learning and Rule-based was proposed for the implementation of our chatbot. We experimented various Machine Learning models and managed to achieve good results, scoring an F1-score of 98.60% with the Random Forest algorithm.