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. 2021 Aug 31:2021:2567080.
doi: 10.1155/2021/2567080. eCollection 2021.

Prediction of Multidrug-Resistant Tuberculosis Using Machine Learning Algorithms in SWAT, Pakistan

Mian Haider Ali  1   2 , Dost Muhammad Khan  1 , Khalid Jamal  2 , Zubair Ahmad  3 , Sadaf Manzoor  4 , Zardad Khan  1
Affiliations

Prediction of Multidrug-Resistant Tuberculosis Using Machine Learning Algorithms in SWAT, Pakistan

Mian Haider Ali et al. J Healthc Eng. .

Retraction in

Abstract

In this paper, we have focused on machine learning (ML) feature selection (FS) algorithms for identifying and diagnosing multidrug-resistant (MDR) tuberculosis (TB). MDR-TB is a universal public health problem, and its early detection has been one of the burning issues. The present study has been conducted in the Malakand Division of Khyber Pakhtunkhwa, Pakistan, to further add to the knowledge on the disease and to deal with the issues of identification and early detection of MDR-TB by ML algorithms. These models also identify the most important factors causing MDR-TB infection whose study gives additional insights into the matter. ML algorithms such as random forest, k-nearest neighbors, support vector machine, logistic regression, leaset absolute shrinkage and selection operator (LASSO), artificial neural networks (ANNs), and decision trees are applied to analyse the case-control dataset. This study reveals that close contacts of MDR-TB patients, smoking, depression, previous TB history, improper treatment, and interruption in first-line TB treatment have a great impact on the status of MDR. Accordingly, weight loss, chest pain, hemoptysis, and fatigue are important symptoms. Based on accuracy, sensitivity, and specificity, SVM and RF are the suggested models to be used for patients' classifications.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Classification accuracy of the techniques based on different number of features.
Figure 2
Figure 2
Variable importance plot (using random forest).
Figure 3
Figure 3
Performance of different classification techniques using feature selection based on LASSO.
Figure 4
Figure 4
Performance of different classification techniques using features selected based on logistic regression.
See this image and copyright information in PMC

References

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