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Prediction and classification of Tuberculosis using machine learning |
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PP: 939-946 |
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doi:10.18576/jsap/130308
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Author(s) |
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Azhari A. Elhag,
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Abstract |
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| Tuberculosis can be fatal and is an infectious disease if it is not treated, that primarily affects the lungs but can also affect other organs in the body. According to the World Health Organization (WHO), tuberculosis is second only to the Covid-19 in terms of the number of deaths it causes and is the thirteenth largest cause of death overall. As a result, it is required to construct predictive models for the incidence and classification of tuberculosis, which aid in identifying the groups and places in which tuberculosis spreads and monitoring the various trends and patterns of tuberculosis. Developing these models is necessary because they help in identifying the groups and locations in which tuberculosis is spread. Artificial network models and decision tree models were used to predict and classify tuberculosis cases in the United States of America using tuberculosis cases data. The results showed that the decision tree model (DT) is more accurate than the artificial neural network (ANN).
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