Information Gain-Based Feature Selection in Support Vector Machines (SVM) for Tuberculosis Prediction
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Abstract
Tuberculosis (TB) is one of the infectious diseases that remains a global health problem, requiring an accurate computational approach to assist in the prediction and clinical decision-making processes. This study aims to evaluate the performance of Support Vector Machine (SVM) in predicting tuberculosis and analyze the contribution of Information Gain (IG)-based feature selection to improving model performance. The dataset used was obtained from the Mendeley public repository and consisted of 493 patient data with 16 encoded attributes. The research stages included data preprocessing, calculation of Information Gain values, selection of relevant features, training of the SVM model with Radial Basis Function (RBF) kernel, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results of the experiment show that the SVM model without feature selection achieved an accuracy of 0.99, with a recall value of 0.97 for the negative class and 1.00 for the positive class. The application of Information Gain produced identical performance, namely an accuracy of 0.99, a macro-average F1-score of 0.99, and a weighted-average F1-score of 0.99. Confusion matrix analysis, both in absolute and normalized forms, showed a very low classification error rate in both scenarios. These findings indicate that the dataset has highly informative features, so feature selection based on Information Gain is not necessary.
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[2] W. H. Organization, Health literacy development for the prevention and control of noncommunicable diseases: volume 2. A globally relevant perspective. World Health Organization, 2022.
[3] K. Lönnroth et al., “Towards tuberculosis elimination: an action framework for low-incidence countries,” Eur. Respir. J., vol. 45, no. 4, pp. 928–952, 2015.
[4] S. Tiberi et al., “Tuberculosis: progress and advances in development of new drugs, treatment regimens, and host-directed therapies,” Lancet Infect. Dis., vol. 18, no. 7, pp. e183–e198, 2018.
[5] P. Rajpurkar, E. Chen, O. Banerjee, and E. J. Topol, “AI in health and medicine,” Nat. Med., vol. 28, no. 1, pp. 31–38, 2022.
[6] G. S. Bisht, D. Ganesh, and S. Kumar, “Detection and Prognosis of Pulmonary Tuberculosis Using ML Classifiers with Soft Computing,” in 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT), IEEE, 2024, pp. 1097–1101.
[7] S. Hansun, A. Argha, S.-T. Liaw, B. Celler, and G. Marks, “Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review,” J. Med. Internet Res., vol. 25, p. e43154, Jul. 2023, doi: 10.2196/43154.
[8] N. Pudjihartono, T. Fadason, A. W. Kempa-Liehr, and J. M. O’Sullivan, “A review of feature selection methods for machine learning-based disease risk prediction,” Front. Bioinforma., vol. 2, p. 927312, 2022.
[9] A. S. Hermiati, R. Herteno, F. Indriani, T. H. Saragih, Muliadi, and T. Triwiyanto, “A Comparative Study: Application of Principal Component Analysis and Recursive Feature Elimination in Machine Learning for Stroke Prediction,” J. Electron. Electromed. Eng. Med. Informatics, vol. 6, no. 3, pp. 231–242, May 2024, doi: 10.35882/jeeemi.v6i3.446.
[10] A. Salhi, R. Alshamrani, A. Althbiti, A. Ismail, M. Abd-ElRahman, and B. M. Hassan, “Optimizing high dimensional data classification with a hybrid AI driven feature selection framework and machine learning schema,” Sci. Rep., vol. 15, no. 1, p. 35038, 2025, doi: 10.1038/s41598-025-08699-4.
[11] A. Al-Nafjan, A. Aljuhani, A. Alshebel, A. Alharbi, and A. Alshehri, “Artificial Intelligence in Predictive Healthcare: A Systematic Review,” J. Clin. Med., vol. 14, no. 19, 2025, doi: 10.3390/jcm14196752.
[12] A. C. Kurniawan and A. Salam, “Seleksi Fitur Information Gain untuk Optimasi Klasifikasi Penyakit Tuberkulosis,” J. Media Inform. Budidarma, vol. 8, no. 1, p. 70, 2024.
[13] J. Brownlee, “Information-gain-and-mutual-information,” Mach. Learn. Mastery, 2020.
[14] J. Brownlee, “Information gain and mutual information for machine learning,” Preuzeto, vol. 18, p. 2020, 2019.
[15] S. B. Kotsiantis, D. Kanellopoulos, and P. E. Pintelas, “Data preprocessing for supervised leaning,” Int. J. Comput. Sci., vol. 1, no. 2, pp. 111–117, 2006.
[16] S. Huang, N. Cai, P. P. Pacheco, S. Narrandes, Y. Wang, and W. Xu, “Applications of support vector machine (SVM) learning in cancer genomics,” Cancer Genomics Proteomics, vol. 15, no. 1, pp. 41–51, 2018.
[17] S. P. Shankar, M. S. Supriya, D. Varadam, M. Kumar, H. Gupta, and R. Saha, “A comprehensive study on algorithms and applications of artificial intelligence in diagnosis and prognosis: AI for healthcare,” in Digital Twins and Healthcare: Trends, Techniques, and Challenges, IGI Global, 2023, pp. 35–54.
[18] M. Sakmar and I. Susilawati, “Identifikasi Jenis Pohon Bambu Berdasarkan Citra Tekstur Daun Berbasis Jaringan Saraf Tiruan dengan Metode Radial Basis Function (RBF),” in Seminar Nasional Multimedia & Artifical Intelligence, 2020, pp. 107–115.
[19] O. Hrizi et al., “Tuberculosis disease diagnosis based on an optimized machine learning model,” J. Healthc. Eng., vol. 2022, no. 1, p. 8950243, 2022.
[20] Y. Liu, “A comparative study on feature selection methods for drug discovery.,” J. Chem. Inf. Comput. Sci., vol. 44, no. 5, pp. 1823–1828, 2004, doi: 10.1021/ci049875d.
[21] Z. Noroozi, A. Orooji, and L. Erfannia, “Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction,” Sci. Rep., vol. 13, no. 1, p. 22588, 2023, doi: 10.1038/s41598-023-49962-w.