An Accurate Integrated System to detect Pulmonary and Extra Pulmonary Tuberculosis using Machine Learning Algorithms

Abstract

Several studies have been reported the use of machine learning algorithms in the detection of Tuberculosis, but studies that discuss the detection of both types of TB, i.e., Pulmonary and Extra Pulmonary Tuberculosis, using machine learning algorithms are lacking. Therefore, an integrated system based on machine learning models has been proposed in this paper to assist doctors and radiologists in interpreting patients’ data to detect of PTB and EPTB. Three basic machine learning algorithms, Decision Tree, Naïve Bayes, SVM, have been used to predict and compare their performance. The clinical data and the image data are used as input to the models and these datasets have been collected from various hospitals of Jalandhar, Punjab, India. The dataset used to train the model comprises 200 patients’ data containing 90 PTB patients, 67 EPTB patients, and 43 patients having NO TB. The validation dataset contains 49 patients, which exhibited the best accuracy of 95% for classifying PTB and EPTB using Decision Tree, a machine learning algorithm.