Performance Evaluation of Popular Supervised Learning Algorithms Towards Cardiovascular Disease
DOI:
https://doi.org/10.32493/informatika.v8i3.34103Keywords:
Cardiovascular Disease, Performance Evaluation, Supervised LearningAbstract
Many studies have discussed the advantages of supervised learning for dealing with extensive data on heart disease. However, only a few studies evaluate the performance of supervised learning algorithms. This research builds a classification model using supervised learning algorithms, including C4.5, Random Forest, Logistic Regression, and Support Vector Machine. The data processed is in the form of category data with character data types. The accuracy, precision, and performance evaluation results show that the Logistic Regression Algorithm has the most superior value compared to the others. On the other hand, it was found that the C4.5 and SVM algorithms had anomalous events. Although the accuracy and precision values of C4.5 were superior to SVM, SVM had better performance.References
Balakrishnan, M., Arockia Christopher, A. B., Ramprakash, P., & Logeswari, A. (2021). Prediction of Cardiovascular Disease using Machine Learning. Journal of Physics: Conference Series, 1767(1), 1–7. https://doi.org/10.1088/1742-6596/1767/1/012013
Braunwald, E. (2019). Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine. In Elsivier (Vol. 7, Number 2).
Building, B., Terrace, L., Kingdom, U., & Systems, I. (n.d.). Decision Tree Learning Based Feature Evaluation and.
Çakmak, H. A., & Demir, M. (2020). Microrna and cardiovascular diseases. Balkan Medical Journal, 37(2). https://doi.org/10.4274/balkanmedj.galenos.2020.2020.1.94
Cherfi, A., Nouira, K., & Ferchichi, A. (2018). Very Fast C4.5 Decision Tree Algorithm. Applied Artificial Intelligence, 32(2), 119–137. https://doi.org/10.1080/08839514.2018.1447479
Ciumărnean, L., Milaciu, M. V., Negrean, V., Orășan, O. H., Vesa, S. C., Sălăgean, O., Iluţ, S., & Vlaicu, S. I. (2022). Cardiovascular risk factors and physical activity for the prevention of cardiovascular diseases in the elderly. In International Journal of Environmental Research and Public Health (Vol. 19, Number 1). https://doi.org/10.3390/ijerph19010207
Djatna, T., Hardhienata, M. K. D., & Masruriyah, A. F. N. (2018). An intuitionistic fuzzy diagnosis analytics for stroke disease. Journal of Big Data, 5(1). https://doi.org/10.1186/s40537-018-0142-7
Hartshorn, S. (2020). Machine Learning with Random Forest and Decision Tree.
Jijo, B. T., & Abdulazeez, A. M. (2021). Classification Based on Decision Tree Algorithm for Machine Learning. Journal of Applied Science and Technology Trends, 2(01), 20–28. https://doi.org/10.38094/jastt20165
Koda, S., Zeggada, A., Melgani, F., & Nishii, R. (2018). Spatial and Structured SVM for Multilabel. Ieee Transactions on Geoscience and Remote Sensing, 1–13.
Komalasari, R., Nurjanah, & Yoche, M. M. (2019). Quality of life of people with cardiovascular disease: A descriptive study. Asian Pacific Island Nursing Journal, 4(2). https://doi.org/10.31372/20190402.1045
Krittanawong, C., Virk, H. U. H., Bangalore, S., Wang, Z., Johnson, K. W., Pinotti, R., Zhang, H. J., Kaplin, S., Narasimhan, B., Kitai, T., Baber, U., Halperin, J. L., & Tang, W. H. W. (2020). Machine learning prediction in cardiovascular diseases: a meta-analysis. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-72685-1
Maharani, A., Sujarwoto, Praveen, D., Oceandy, D., Tampubolon, G., & Patel, A. (2019). Cardiovascular disease risk factor prevalence and estimated 10-year cardiovascular risk scores in Indonesia: The SMARThealth Extend study. PLoS ONE, 14(4). https://doi.org/10.1371/journal.pone.0215219
Mahmoud, M. A. I., & Ren, H. (2019). Forest fire detection and identification using image processing and SVM. Journal of Information Processing Systems, 15(1), 159–168. https://doi.org/10.3745/JIPS.01.0038
Maiga, J., Hungilo, G. G., & Pranowo. (2019). Comparison of Machine Learning Models in Prediction of Cardiovascular Disease Using Health Record Data. Proceedings - 1st International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2019, 45–48. https://doi.org/10.1109/ICIMCIS48181.2019.8985205
Masruriyah, A. F. N., Djatna, T., Dewi Hardhienata, M. K., Handayani, H. H., & Wahiddin, D. (2019). Predictive Analytics For Stroke Disease. Proceedings of 2019 4th International Conference on Informatics and Computing, ICIC 2019. https://doi.org/10.1109/ICIC47613.2019.8985716
Mathur, P., Srivastava, S., Xu, X., & Mehta, J. L. (2020). Artificial Intelligence, Machine Learning, and Cardiovascular Disease. In Clinical Medicine Insights: Cardiology (Vol. 14). https://doi.org/10.1177/1179546820927404
Mezzatesta, S., Torino, C., De Meo, P., Fiumara, G., & Vilasi, A. (2019). A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis. Computer Methods and Programs in Biomedicine, 177, 9–15. https://doi.org/10.1016/j.cmpb.2019.05.005
Mia, M., Masruriyah, A. F. N., & Pratama, A. R. (2022). The Utilization of Decision Tree Algorithm In Order to Predict Heart Disease. JURNAL SISFOTEK GLOBAL, 12(2), 138. https://doi.org/10.38101/sisfotek.v12i2.551
Ministry of Health Republic of Indonesia. (2014). Penyakit Tidak Menular. http://www.depkes.go.id/folder/view/01/structure-publikasi-pusdatin-buletin.html
National Center for Chronic Disease Prevention and Health Promotion Division for Heart Disease and Stroke Prevention. (2022, June). Heart Disease Maps and Data Sources. Centers for Disease Control and Prevention. https://www.cdc.gov/heartdisease/statistical_reports.htm
Noor, N. Bin, Anwar, M. S., & Dey, M. (2019). Comparative Study between Decision Tree, SVM and KNN to Predict Anaemic Condition. BECITHCON 2019 - 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health, December, 24–28. https://doi.org/10.1109/BECITHCON48839.2019.9063188
Padmanabhan, M., Yuan, P., Chada, G., & Van Nguyen, H. (2019). Physician-friendly machine learning: A case study with cardiovascular disease risk prediction. Journal of Clinical Medicine, 8(7). https://doi.org/10.3390/jcm8071050
Primajaya, A., & Sari, B. N. (2018). Random Forest Algorithm for Prediction of Precipitation. Indonesian Journal of Artificial Intelligence and Data Mining, 1(1), 27. https://doi.org/10.24014/ijaidm.v1i1.4903
Setyaji, D. Y., Prabandari, Y. S., & Gunawan, I. M. A. (2018). Aktivitas fisik dengan penyakit jantung koroner di Indonesia The relationships of physical activity with coronary heart disease in Indonesia. Jurnal Gizi Klinik Indonesia, 14(3), 115–121. https://jurnal.ugm.ac.id/jgki
Sofiana, L., Rokhmayanti, R., Sulistyawati, Nurfita, D., Astuti, F. D., & Sholekhati, P. A. (2021). Evaluation of cardiovascular disease program in Sleman district, Indonesia. International Journal of Public Health Science, 10(2). https://doi.org/10.11591/ijphs.v10i2.20492
Utami, R. Y. (2017). Pengembangan Media Ajar Anatomi Jantung dengan Low Cost Material Development of Anatomy of Heart Learning Resource with Low Cost Material. 2(1).
World Health Organization. (2019). Cardiovascular diseases (CVDs). who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
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