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Application of artificial intelligence techniques for the detection of pulmonary tuberculosis in Colombia

10.24050/reia.v20i39.1617
Published 2022-12-20
Articles

Abstract

Tuberculosis is a respiratory disease that affects lungs and it is caused by the bacillus Mycobacterium tuberculosis (MTB), which is spread when people who are sick with tuberculosis expel bacteria into the air by coughing. Before the coronavirus (COVID-19) pandemic, tuberculosis was the leading cause of death from infectious agents even ranking above HIV/AIDS. Growing MTB on solid medium is the main diagnostic reference method. In addition, this is the standard method for identifying the bacterial resistance profile. However, the waiting period for the results is long, during which time a patient can be highly contagious. In turn, delaying treatment for tuberculosis can increase disease severity, but treating without diagnostic confirmation can lead to bacterial resistance, a higher rate of adverse events, and higher costs. Therefore, pulmonary tuberculosis disease must be rapidly diagnosed by a cost-effective method. This paper proposes models for the detection of pulmonary tuberculosis by using different artificial intelligence techniques. These models can be used to support decision making of doctors and are intended to identify whether a patient has tuberculosis or not. In particular, four supervised learning techniques are used (neural networks, decision trees, and two ensemble methods). Each model allows predicting a positive or negative diagnosis of pulmonary tuberculosis based on previously recorded diagnostic variables taken from patients in Cali, Colombia. According to the results, the Extra Trees method reaches the highest accuracy compared to the other techniques used for the prediction of pulmonary tuberculosis with an area under the ROC curve of 95.63%.

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Authors

  • Oscar Fernando Bedoya Leiva

    Colombia.

    https://orcid.org/0000-0002-9378-7337

  • Harry Santiago Guarín Aristizábal

    Colombia.

  • Jared Zayiri Agudelo Delgado

    Colombia.

References

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