Development of Chatbot System by Using Decision Tree Algorithm for Medical Specialist

Abstract

The rapid advancement of technology has a big impact on how we live our daily lives. Artificial Intelligence (AI), or more precisely, an AI-powered chatbot, is one of those technologies. This virtual assistant has gained a lot of popularity recently, mostly because of noteworthy advancements in artificial intelligence, machine learning, and other basic fields like neural networks and natural language processing. These chatbots use interactive questions to efficiently converse with everyone. Additionally, hospitals are starting to incorporate online components so that people can learn more about the facility and some of the services it offers. The objective of this project is to use decision tree algorithms to create an AI chatbot system for medical specialists. Based on the input of symptoms and comorbidities, the system will be compared to another algorithm to evaluate which one performs better. In this study, two machine learning algorithm models—the DT classifier and the RF classifier— were constructed and trained. Each model's accuracy score is used to assess it. Based on the findings, it is recommended to utilise the RF algorithm rather than the DT method for the Chatbot Dataset because it has a higher accuracy score.