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Background: Mycobacterium Tuberculosis (TB) is an infectious bacterialdisease presenting similar symptoms to pneumonia; therefore, differentiatingbetween TB and pneumonia is challenging. Therefore, the main aim of this studyis proposing an automatic method for differential diagnosis of TB fromPneumonia. Methods: In this study, a two-step decision support system namedTPIS is proposed for differential diagnosis of TB from pneumonia based onstacked ensemble classifiers. The first step of our proposed model aims atearly diagnosis based on low-cost features including demographiccharacteristics and patient symptoms (including 18 features). TPIS second stepmakes the final decision based on the meta features extracted in the firststep, the laboratory tests and chest radiography reports. This retrospectivestudy considers 199 patient medical records for patients suffering from TB orpneumonia, which has been registered in a hospital in Arak, Iran. Results:Experimental results show that TPIS outperforms the compared machine learningmethods for early differential diagnosis of pulmonary tuberculosis frompneumonia with AUC of 90.26 and accuracy of 91.37 and final decision makingwith AUC of 92.81 and accuracy of 93.89. Conclusions: The main advantage ofearly diagnosis is beginning the treatment procedure for confidently diagnosedpatients as soon as possible and preventing latency in treatment. Therefore,early diagnosis reduces the maturation of late treatment of both diseases.

Proposing a two-step Decision Support System (TPIS) based on Stacked  ensemble classifier for early and low cost (step-1) and final (step-2)  differential diagnosis of Mycobacterium Tuberculosis from non-tuberculosis  Pneumonia