Comparison of the predictive outcomes for anti-tuberculosis drug-induced hepatotoxicity by different machine learning techniques. (May 2020)

• Record Type: Journal Article
• Authors: Lai, Nai-Hua; Shen, Wan-Chen; Lee, Chun-Nin; Chang, Jui-Chia; Hsu, Man-Ching; Kuo, Li-Na; Yu, Ming-Chih; Chen, Hsiang-Yin
• Is Part Of: Computer methods and programs in biomedicine. Volume 188(2020)
• Publication Date: 2020-05
• Subjects: Tuberculosis -- Anti-tuberculosis drugs -- Gene polymorphism -- Artificial neural network -- Support vector machine -- Random forest -- Feature selection; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105307
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The study compared the performance of artificial neural network, support vector machine and random forest on predicting anti-tuberculosis drugs induced hepatotoxicity. The best performance to predict anti-tuberculosis drugs induced hepatotoxicity was generated by artificial neural network among three bio-prospecting techniques. Combining genomic and clinical data can further increase the area under receiver operating characteristic curve than using genomic or clinical data alone. Abstract: Background: The study compared the predictive outcomes of artificial neural network, support vector machine and random forest on the occurrence of anti-tuberculosis drug-induced hepatotoxicity. Methods: The clinical and genomic data of patients treated with anti-tuberculosis drugs at Taipei Medical University-Wanfang Hospital were used as training sets, and those at Taipei Medical University-Shuang Ho Hospital served as test sets. Features were selected through a univariate risk factor analysis and literature evaluation. The accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve were calculated to compare the traditional, genomic, and combined models of the three techniques. Results: Nine models were created with 7 clinical factors and 4 genotypes. Artificial neural network with clinical and genomic factors exhibited the best performance, with an accuracy of 88.67%, a sensitivity of 80%, and a specificity of 90.4% for the test set. The area under the receiver operating characteristic curve of this best model reached 0.894 for training set and 0.898 for test set, which was significantly better than 0.801 for training set and 0.728 for test set by support vector machine and 0.724 for training set and 0.718 for test set by random forest. Conclusions: Artificial neural network with clinical and genomic data can become a clinical useful tool in predicting anti-tuberculosis drug-induced hepatotoxicity. The machine learning technique can be an innovation to predict and prevent adverse drug reaction.