Support Vector Machine for Classifying Heart Failure, Hypertension, and Normal Heart Condition

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Jusifov11i1, june 2025
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Published: Jun 30, 2025
DOI: https://doi.org/10.19109/jusifo.v11i1.28113
Keywords:
Heart disease prediction, machine learning in healthcare, support vector machine

Main Article Content

Surya Amando Bangun
Department of Information System, Universitas Prima Indonesia
Elvis Sastra Ompusunggu
Department of Information System, Universitas Prima Indonesia
Wilson Wilson
Department of Information System, Universitas Prima Indonesia
Eppi Kriawati Harefa
Department of Information System, Universitas Prima Indonesia

Abstract

Cardiovascular diseases, particularly heart failure and hypertension, remain among the leading causes of global mortality, underscoring the urgent need for accurate early diagnosis. This study proposes a classification model based on the Support Vector Machine (SVM) algorithm to distinguish among heart failure, hypertension, and normal heart conditions using real-world clinical data. The dataset was preprocessed through normalization and nominal-to-numerical conversion and validated by medical experts to ensure data quality. K-Fold Cross Validation (K=10) was employed to ensure model robustness and mitigate overfitting. The SVM classifier utilized a linear kernel and achieved high performance in terms of accuracy, precision, and recall. The results demonstrate the effectiveness of the proposed model in classifying multiple cardiovascular conditions with clinically relevant input features. This research contributes to the advancement of intelligent diagnostic tools and supports the integration of machine learning into clinical decision-making processes.

Article Details

How to Cite
Bangun, S. A., Ompusunggu, E. S. ., Wilson, W., & Harefa, E. K. (2025). Support Vector Machine for Classifying Heart Failure, Hypertension, and Normal Heart Condition. JUSIFO (Jurnal Sistem Informasi), 11(1), 53-60. https://doi.org/10.19109/jusifo.v11i1.28113
Issue
Vol. 11 No. 1 (2025): JUSIFO (Jurnal Sistem Informasi) | June 2025
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Articles
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How to Cite

Bangun, S. A., Ompusunggu, E. S. ., Wilson, W., & Harefa, E. K. (2025). Support Vector Machine for Classifying Heart Failure, Hypertension, and Normal Heart Condition. JUSIFO (Jurnal Sistem Informasi), 11(1), 53-60. https://doi.org/10.19109/jusifo.v11i1.28113

References

Abdullah, D. M., & Abdulazeez, A. M. (2021). Machine learning applications based on svm classification a review. Qubahan Academic Journal, 1(2), 81–90. https://doi.org/10.48161/QAJ.V1N2A50

Amarappa, S., & Sathyanarayana, S. V. (2014). Data classification using support vector machine (svm), a simplified approach. International Journal of Electronics and Computer Science Engineering, 3(4).

Carey, R. M., Whelton, P. K., Aronow, W. S., Casey, D. E., Collins, K. J., Himmelfarb, C. D., DePalma, S. M., Gidding, S., Jamerson, K. A., Jones, D. W., McLaughlin, E. J., Muntner, P., Ovbiagele, B., Smith, S. C., Spencer, C. C., Stafford, R. S., Taler, S. J., Thomas, R. J.,

Williams, K. A., … Wright, J. T. (2018). Prevention, detection, evaluation, and management of high blood pressure in adults: synopsis of the 2017 american college of cardiology/american heart association hypertension guideline. Annals of Internal Medicine, 168(5), 351–358. https://doi.org/10.7326/M17-3203

Chong, B., Jayabaskaran, J., Jauhari, S. M., Chan, S. P., Goh, R., Kueh, M. T. W., Li, H., Chin, Y. H., Kong, G., Anand, V. V., Wang, J.-W., Muthiah, M., Jain, V., Mehta, A., Lim, S. L., Foo, R., Figtree, G. A., Nicholls, S. J., Mamas, M. A., … Chan, M. Y. (2024). Global burden of cardiovascular diseases: projections from 2025 to 2050. European Journal of Preventive Cardiology. https://doi.org/10.1093/EURJPC/ZWAE281

Cifu, A. S., & Davis, A. M. (2017). Prevention, detection, evaluation, and management of high blood pressure in adults. JAMA, 318(21), 2132–2134. https://doi.org/10.1001/JAMA.2017.18706

Galindra, Y., Astiah, A. A., & Nuralyfah, N. S. (2024). Hubungan antara derajat hipertensi dengan kualitas tidur pada pasien hipertensi di rumah sakit pmi kota bogor. Zona Kedokteran: Program Studi Pendidikan Dokter Universitas Batam, 14(2). https://doi.org/10.37776/ZKED.V14I2.1533

Ghasemi, F., & Sharifi, S. (2025). Heart failure prediction using support vector machine. International Journal of Novel Research in Life Sciences, 25(1).

Hidayaturrohman, Q. A., & Hanada, E. (2024). Impact of data pre-processing techniques on xgboost model performance for predicting all-cause readmission and mortality among patients with heart failure. BioMedInformatics, 4(4), 2201–2212. https://doi.org/10.3390/BIOMEDINFORMATICS4040118

Khan, A., Qureshi, M., Daniyal, M., & Tawiah, K. (2023). A novel study on machine learning algorithm-based cardiovascular disease prediction. Health & Social Care in the Community, 2023(1), 1406060. https://doi.org/10.1155/2023/1406060

Kumar, R., Garg, S., Kaur, R., Johar, M. G. M., Singh, S., Menon, S. V., Kumar, P., Hadi, A. M., Hasson, S. A., & Lozanović, J. (2025). A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions. Frontiers in Artificial Intelligence, 8, 1583459. https://doi.org/10.3389/FRAI.2025.1583459

Liu, Z. “Leo.” (2025). Support vector machines. In Artificial Intelligence for Engineers (pp. 129–140). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-75953-6_5

Patle, A., & Chouhan, D. S. (2013). Svm kernel functions for classification. 2013 International Conference on Advances in Technology and Engineering, ICATE 2013. https://doi.org/10.1109/ICADTE.2013.6524743

Plati, D. K., Tripoliti, E. E., Bechlioulis, A., Rammos, A., Dimou, I., Lakkas, L., Watson, C., McDonald, K., Ledwidge, M., Pharithi, R., Gallagher, J., Michalis, L. K., Goletsis, Y., Naka, K. K., & Fotiadis, D. I. (2021). A machine learning approach for chronic heart failure diagnosis. Diagnostics 2021, 11(10), 1863. https://doi.org/10.3390/DIAGNOSTICS11101863

Saleem, A., Asif, K. H., Ali, A., Awan, S. M., & Alghamdi, M. A. (2014). Pre-processing methods of data mining. Proceedings - 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, UCC 2014, 451–456. https://doi.org/10.1109/UCC.2014.57

Sandhya, Y. (2020). Prediction of heart diseases using support vector machine. 8. https://doi.org/10.22214/ijraset.2020.2021

Shihong, Y., Ping, L., & Peiyi, H. (2003). Svm classification: its contents and challenges. Applied Mathematics, 18(3), 332–342. https://doi.org/10.1007/S11766-003-0059-5

Shmilovici, A. (2023). Support vector machines. In Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition (pp. 93–110). Springer International Publishing. https://doi.org/10.1007/978-3-031-24628-9_6

Srivastava, D. K., & Bhambhu, L. (2010). Data classification using support vector machine. Journal of Theoretical and Applied Information Technology, 12(1).

Suryadi, S., Solikin, S., & Uni, U. (2024). Analisa faktor risiko komplikasi gagal jantung pada pasien hipertensi di rsud ulin banjarmasin. Jurnal Keperawatan Suaka Insan (JKSI), 9(2), 142–148. https://doi.org/10.51143/JKSI.V9I2.708

Wan, S., Wan, F., & Dai, X. jian. (2025). Machine learning approaches for cardiovascular disease prediction: A review. Archives of Cardiovascular Diseases. https://doi.org/10.1016/J.ACVD.2025.04.055

World Health Organization. (2021). Cardiovascular diseases (CVDs). https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)

Yu, H., & Kim, S. (2012). Svm tutorial — classification, regression and ranking. In Handbook of Natural Computing (Vols. 1–4, pp. 479–506). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_15

Yuniarti, E. (2022). Epidemiologi gagal jantung. https://www.alomedika.com/penyakit/kardiologi/gagal-jantung/epidemiologi