Prediction of Tuberculosis on HIV Patients Based on Gene Expression Data Using Grey Wolf Optimization-Support Vector Machine

Hana Amani Fatihah, Hasmawati, I. Kurniawan
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Abstract

The main cause of Tuberculosis (TB), a specific infectious disease that affects people worldwide, is Mycobacterium Tuberculosis (MTB). An estimated 30% of the population worldwide has a TB infection, which causes over 20 million deaths annually. Also, 37.7 million people are afflicted with HIV and TB together. Detecting TB in HIV patients is crucial due to the high risk associated with TB. To identify HIV-positive patients, RNA-based methods are used to find host gene expression signatures associated with different aspects of the disease. Nevertheless, no group in this method describes gene signatures that can be used to identify patients who are co-infected with TB and HIV. Therefore, a method is needed to identify TB in HIV patients. This study aims to classify high-dimensional micro array data using Grey Wolf Optimization (GWO) with Support Vector Machines (SVM). To improve the performance of the model, hyperparameter tuning was carried out. Based on the results, we obtained the optimal SVM model using a linear kernel that outperforms other kernels in terms of accuracy, with Fl-score values of 0.78 and 0.80, respectively.
利用灰狼优化-支持向量机,基于基因表达数据预测艾滋病患者的结核病病情
结核病(TB)是一种影响全世界人民的特殊传染病,其主要病因是结核分枝杆菌(MTB)。据估计,全球有 30% 的人感染了结核病,每年造成 2 000 多万人死亡。此外,有 3770 万人同时患有艾滋病和结核病。由于与结核病相关的高风险,在艾滋病患者中检测结核病至关重要。为了识别 HIV 阳性患者,人们使用基于 RNA 的方法来寻找与疾病不同方面相关的宿主基因表达特征。然而,在这种方法中,没有一个小组描述了可用于识别结核病和艾滋病病毒双重感染患者的基因特征。因此,需要一种方法来识别艾滋病病毒感染者的结核病。本研究旨在使用灰狼优化(GWO)和支持向量机(SVM)对高维微阵列数据进行分类。为了提高模型的性能,我们进行了超参数调整。根据结果,我们得到了使用线性内核的最佳 SVM 模型,其准确性优于其他内核,Fl-score 值分别为 0.78 和 0.80。
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