Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Peradeniya, Sri Lanka.
Int J Mycobacteriol. 2021 Jul-Sep;10(3):279-284. doi: 10.4103/ijmy.ijmy_130_21.
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.
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.
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.
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.
结核病（TB）是一种主要影响肺部的疾病，如果治疗延误，可能致命。本研究聚焦于导致耐药性的特征，对结核病患者的治疗失败进行预测。
支持向量机（SVM）是一种相对较新的分类模型，在回归应用中表现出了有前途的性能。遗传算法（GA）是一种用于解决优化问题的方法。我们考虑了从缅甸仰光的结核病阳性患者那里收集的与生活方式和治疗偏好相关的数据，以更清楚地了解结核病耐药性。在本文中，使用 SVM 分类器对结核病耐药性进行了分析和建模。GA 用于通过从 35 个完整特征集中选择最适合的 20 个特征来增强 SVM 的整体性能。进一步研究了 SVM 模型的四种不同核函数的性能，以获得最佳性能。
使用 SVM 和 GA 训练模型后，我们将未见过的数据输入到模型中，以预测患者的治疗耐药性。结果表明，GA 增强的 SVM 仅使用 20 个特征即可实现 67%的未见过数据治疗耐药性预测准确性。
这些发现反过来将有助于根据患者的生活方式选择和社会环境制定有效的结核病治疗计划。此外，本研究中开发的模型将来可以推广到预测许多疾病的药物治疗结果。
