An Intelligent Chatbot Using NLP and TF-IDF Algorithm for Text Understanding Applied to the Medical Field

Abstract

This is the era of intelligent machines. Due to the progress and advancements of machine learning, deep learning, and artificial intelligence, machines are impersonating humans. Natural language processing-based conversational software agents are known as chatbots. These chatbots are the best example of such machines. To spend a healthy life, health care is much important. But it is not easy to have a consultation with the appropriate doctor while having health problems. Our proposed solution for this problem is to create an artificial intelligence-based medical chatbot, which will be able to detect the ailment and also to give the necessary details about any condition. The purpose of this chatbot is to minimize the healthcare cost and to improve the approachability to medical knowledge. This chatbot will act as a medical reference book that will help the patients to diagnose their sickness and also to recover their health. A patient can only gain the actual benefit of the chatbot only when a chatbot can diagnose the accurate disease and also provide the necessary information about the condition. A text-to-text verdict bot involves people in the discussion about their health problems and also provides a personalized diagnosis depending on the symptoms. Hence, people will get ideas related to their health conditions and will get the right safety. In this work, we performed a detailed survey on recent literature. We examined many publications from the last 5 years, which are related to chatbots. We also presented a hybrid architecture based on deep learning models like NLP and the TF-IDF algorithm.