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ORIGINAL ARTICLE
Year : 2021  |  Volume : 10  |  Issue : 3  |  Page : 279-284

Prediction of treatment failure of tuberculosis using support vector machine with genetic algorithm


Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Peradeniya, Sri Lanka

Correspondence Address:
Maheshi B Dissanayake
Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Peradeniya
Sri Lanka
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijmy.ijmy_130_21

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Background: Tuberculosis (TB) is a disease that mainly affects human lungs. It can be fatal if the treatment is delayed. This study investigates the prediction of treatment failure of TB patients focusing on the features which contributes mostly for drug resistance. Methods: Support vector machine (SVM) is a relatively novel classification model that has shown promising performance in regression applications. Genetic algorithm (GA) is a method for solving the optimization problems. We have considered lifestyle and treatment preference-related data collected from TB-positive patients in Yangon, Myanmar to obtain a clear picture of the TB drug resistance. In this article, TB drug resistance is analyzed and modelled using SVM classifier. GA is used to enhance the overall performance of SVM, by selecting the most suitable 20 features from the 35 full feature set. Further, the performance of four different kernels of SVM model is investigated to obtain the best performance. Results: Once the model is trained with SVM and GA, we have feed unseen data into the model to predict the treatment resistance of the patient. The results have shown that SVM with GA is capable of achieving 67% of accuracy in predicting the treatment resistance in unseen data with only 20 features. Conclusion: The findings would in turn, assist to develop an effective TB treatment plan in future based on patients' lifestyle choices and social settings. In addition, the model developed in this research can be generalized to predict the outcome of drug therapy for many diseases in future.


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