Smart Expert System for Tuberculosis Diagnosis Using the Naïve Bayes Method

Abstract

Tuberculosis (TB) remains one of the major infectious diseases worldwide, requiring accurate early diagnosis to reduce transmission and improve treatment outcomes. Although numerous machine learning-based diagnostic systems have been developed, most previous studies have primarily focused on improving classification accuracy without integrating an expert system to support clinical decision-making. This study aims to develop a Smart Expert System for the early diagnosis of tuberculosis using the Naïve Bayes algorithm. The novelty of this research lies in the integration of the Naïve Bayes algorithm with a web-based expert system that not only generates diagnostic predictions but also provides recommendation-based decision support. The proposed system was developed using 200 tuberculosis patient records consisting of demographic data and clinical symptoms. The research process included data preprocessing, model training, system implementation, and performance evaluation using Accuracy, Precision, Recall, and F1-score metrics. The experimental results achieved an Accuracy of 87.00%, Precision of 85.00%, Recall of 88.00%, and an F1-score of 86.00%, indicating reliable classification performance. The developed system assists healthcare professionals in conducting preliminary TB screening, accelerating clinical decision-making, and improving public access to early tuberculosis diagnostic information.