Applying several machine learning models to tuberculosis dataset and evaluating the results based on models accuracy.

Tuberculosis is an infectious disease that spreads through the air when someone sneezes or coughs, and it mainly affects the lungs. It is a very common disease, prevailing mainly in undeveloped and developing nations. Inflammation of the lungs Tuberculosis can be diagnosed with the help of radiology by taking a chest X-ray of the patient and a sample of sputum to check whether bacteria (Mycobacterium) exist or not. The techniques of artificial intelligence can help in the diagnosis and fast predictions of tuberculosis. In this research paper, we analysed the dataset of tuberculosis patents by applying the classification models of machine learning. The accuracy of models will be compared by varying the training and testing datasets. The CPU execution time of the classification models is also calculated, to determine which model performs faster. [ABSTRACT FROM AUTHOR]