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. 2021 Jul 20;11(1):14806.
doi: 10.1038/s41598-021-94347-6.

Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis

Victor Chukwudi Osamor  1 , Adaugo Fiona Okezie  2
Affiliations

Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis

Victor Chukwudi Osamor et al. Sci Rep. .

Abstract

Tuberculosis has the most considerable death rate among diseases caused by a single micro-organism type. The disease is a significant issue for most third-world countries due to poor diagnosis and treatment potentials. Early diagnosis of tuberculosis is the most effective way of managing the disease in patients to reduce the mortality rate of the infection. Despite several methods that exist in diagnosing tuberculosis, the limitations ranging from the cost in carrying out the test to the time taken to obtain the results have hindered early diagnosis of the disease. This work aims to develop a predictive model that would help in the diagnosis of TB using an extended weighted voting ensemble method. The method used to carry out this research involved analyzing tuberculosis gene expression data obtained from GEO (Transcript Expression Omnibus) database and developing a classification model to aid tuberculosis diagnosis. A classifier combination of Naïve Bayes (NB), and Support Vector Machine (SVM) was used to develop the classification model. The weighted voting ensemble technique was used to improve the classification model's performance by combining the classification results of the single classifier and selecting the group with the highest vote based on the weights given to the single classifiers. Experimental analysis indicates a performance accuracy of the enhanced ensemble classifier as 0.95, which showed a better performance than the single classifiers, which had 0.92, and 0.87 obtained from SVM and NB, respectively. The developed model can also assist health practitioners in the timely diagnosis of tuberculosis, which would reduce the mortality rate caused by the disease, especially in developing countries.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
An ensemble architecture.
Figure 2
Figure 2
Weighted voting algorithm.
Figure 3
Figure 3
Extended weighted voting ensemble algorithm.
Figure 4
Figure 4
Model architecture for EWVA.
Figure 5
Figure 5
Plot of RFE-CV result.
Figure 6
Figure 6
Visual representation of SVM classifier’s confusion matrix.
Figure 7
Figure 7
Visual representation of NB classifier’s confusion matrix.
Figure 8
Figure 8
Visual representation of existing weighted voting ensemble method confusion matrix.
Figure 9
Figure 9
Visual representation of improved weighted voting ensemble method confusion matrix.
See this image and copyright information in PMC

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