Early Diagnosis of Eye Disease Using an Expert System-Based Chatbot
Abstract
Chatbots have emerged as popular tools across various domains, including expert systems for disease diagnosis. This research aims to develop a chatbot leveraging the Naive Bayes method within an expert system for diagnosing eye diseases. The Naive Bayes method was chosen for its efficiency in handling data classification and its ability to provide the necessary class probabilities in diagnosis. The resulting chatbot is designed to simplify the diagnosis process for users by providing a user-friendly and easily understandable interface. Evaluation of the system demonstrated an 87% accuracy rate in initial diagnoses when compared against specialist evaluations. Additionally, the User Acceptance Test revealed a high acceptance rate, with an average score of 84.75%, indicating strong user satisfaction with the system’s performance and ease of use. These findings suggest that deploying a chatbot with the Naive Bayes method in an expert system for diagnosing eye diseases has the potential to serve as a valuable platform in supporting medical practitioners in diagnosing eye diseases more efficiently and accurately.
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