Optimization of Tuberculosis Diagnosis Using the Support Vector Machine Method on Health Data of Central Sulawesi Province
Abstract
Tuberculosis (TB) is a disease caused by infection with the bacterium Mycobacterium tuberculosis in the lungs. TB is a major global health problem, ranking as the second leading cause of death from infectious diseases worldwide. Given that TB is one of the infectious diseases that remains a public health problem, early and accurate detection is key in controlling its spread. This study uses historical data of TB patients from several hospitals in Central Sulawesi as research samples. One of the classification methods is the Support Vector Machine (SVM) method. This method was chosen because of its ability to classify non-linear data and its potential to produce high prediction accuracy with complex data. This study aims to improve the accuracy and efficiency of Tuberculosis (TB) disease diagnosis in Central Sulawesi Province through the application of the SVM method. The parameters used in this study were age, gender, body temperature, shortness of breath, chest pain, sputum examination, and final diagnosis. The results of this study show that handling the problem of imbalanced data with an approach at the data level using the Adaptive Synthetic Sampling (adasyn) method approach and SVM as a classification method for two classes in this study shows that this method can classify each research data used with an accuracy rate of more than 95%. The classification results show that the larger the data used for testing, the greater the classification accuracy. The best classification accuracy value is obtained from the scheme 4 division of training and testing data, namely 90%:10% with a value of 100%. This shows that the method used in this study can be used to help diagnose TB disease based on a patient's medical data. From this research, we introduce a web-based information system that can be used to conduct early detection for TB patients independently, where they are unwilling or embarrassed to come to the hospital.
Keywords: TB disease, Classification, Support Vector Machine, accuracy, Web-based application information system.
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WHO, Global Tuberculosis Report. 2023.
WAHID W.W.C., ARSY F. F., SETIAWATI N., VICRI R. N., MIRANDA A.V., LESTARI B.W., and APRILLIA K.R. Increasing Financing for Tuberculosis Programs in Indonesia. RDI Policy Brief., 2023, 6, 20230619.
RIZKANINGSIH R., and MUSTAFA M. Hubungan Kondisi Fisik Lingkungan Rumah dengan Kejadian TBC (Tuberculosis). Jurnal Promotif Preventif, 2023, 6(2): 335-343. https://doi.org/10.47650/jpp.v6i2.784
REA, G., SPERANDEO, M., LIETO, R., BOCCHINO, M., QUARATO, C. M. I., FERAGALLI, B., VALENTE, T., SCIOSCIA, G., GIUFFREDA, E., FOSCHINO BARBARO, M. P., & LACEDONIA, D. Chest Imaging in the Diagnosis and Management of Pulmonary Tuberculosis: The Complementary Role of Thoraci Ultrasound. Frontiers in Medicine, 2021, 8: 753821. https://doi.org/10.3389/fmed.2021.753821
VAPNIK V. N. The Nature of Statistical Learning Theory. Springer, N.Y., 2000. https://doi.org/10.1007/978-1-4757-3264-1
SAIN H. ,and PURNAMI S. W. Combine Sampling Support Vector Machine for Imbalanced Data Classification. Procedia Computer Science, 2015, 72: 59–66, https://doi.org/10.1016/j.procs.2015.12.105
CERVANTES J., GARCIA-LAMONT F., RODRÍGUEZ-MAZAHUA L., and LOPEZ A. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 2020, 408: 189–215, https://doi.org/10.1016/j.neucom.2019.10.118
HAMED B. A., IBRAHIM O. A. S., and ABD EL-HAFEEZ T. Optimizing classification efficiency with machine learning techniques for pattern matching. Journal of Big Data, 2023, 10(1): 124, https://doi.org/10.1186/s40537-023-00804-6
FIBRIANTO A., MAGDALENA R., & FUADAH Y.N. Classification of normal pulmonary conditions, tuberculosis disease (tbc) and pleura effusion on humans using artificial neural network-Backpropagation. e-Proceeding of Engineering, 2018, 5(3): 5071–5078. https://core.ac.uk/reader/299924570
WARDANI R. S., and PURWANTO. Model diagnosis tuberkulosis menggunakan k-nearest neighbor berbasis seleksi atribut External Monitoring Mission. Proceedings of the 2nd University Research Coloquium, 2015, 284-290. https://jurnal.unimus.ac.id/index.php/psn12012010/article/view/1601
SHOFIA E. N., REGASARI R., PUTRI M., and ARWAN A. Sistem Pakar Diagnosis Penyakit Demam : DBD, Malaria dan Tifoid Menggunakan Metode K-Nearest Neighbor – Certainty Factor. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2017, 1(5), 426–435. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/122
AINI N. and HATTA H. R. Sistem pakar pendiagnosa penyakit tuberkulosis. Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, 2017, 12(1): 56–63. http://dx.doi.org/10.30872/jim.v12i1.224
CUDAHY P., and SHENOI S. V Diagnostics for pulmonary tuberculosis. Postgraduate Medical Journal, 2016, 92(1086): 187–193, https://doi.org/10.1136/postgradmedj-2015-133278
LEWINSOHN, D. M., LEONARD, M. K., LOBUE, P. A., COHN, D. L., DALEY, C. L., DESMOND, E., KEANE, J., LEWINSOHN, D. A., LOEFFLER, A. M., … & WOODS, G. L. Official American Thoracic Society/Infectious Diseases Society of America/Centers for Disease Control and Prevention Clinical Practice Guidelines: Diagnosis of Tuberculosis in Adults and Children. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America, 2017, 64(2), e1–e33. https://doi.org/10.1093/cid/ciw694
CHENG, J., SUN, Y. N., ZHANG, C. Y., YU, Y. L., TANG, L. H., PENG, H., PENG, Y., YAO, Y. X., HOU, S. Y., LI, J. W., ZHAO, J. M., XIA, L., XU, L., XIA, Y. Y., ZHAO, F., WANG, L. X., & ZHANG, H. Incidence and risk factors of tuberculosis among the elderly population in China: a prospective cohort study. Infectious diseases of poverty, 2020, 9(1), 13. https://doi.org/10.1186/s40249-019-0614-9
GEBREWELD, F. H., KIFLE, M. M., GEBREMICHEAL, F. E., SIMEL, L. L., GEZAE, M. M., GHEBREYESUS, S. S., MENGSTEAB, Y. T., & WAHD, N. G. Factors influencing adherence to tuberculosis treatment in Asmara, Eritrea: a qualitative study. Journal of Health, Population, and Nutrition, 2018, 37(1), 1. https://doi.org/10.1186/s41043-017-0132-y
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