Implementasi Machine Learning Untuk Prediksi Penyakit Jantung Menggunakan Algoritma Support Vector Machine
Abstract
Heart disease is currently a disease that has taken over many human lives. Data shows that more than 17 million people have died from heart disease. The high number of deaths, therefore, requires special handling to treat and prevent heart disease. In the development of technology, diagnosis of heart disease can be done with the help of information technology, one of which is through machine learning. This study aims to implement machine learning through the SVM algorithm to predict heart disease. The model formed by SVM produces an evaluation value indicated by an accuracy value of 0.85, a precision of 0.93, a recall of 0.76, and an f-1 score of 0.83. This model is used as training data to predict heart disease which is then successfully used to create a system through the Streamlit library which can be easily accessed via the website.
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References
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Aendikov, N., Azayeva, A., 2024. Integration of GIS and machine learning analytics into Streamlit application. Procedia Comput. Sci. 231, 691–696. https://doi.org/10.1016/j.procs.2023.12.160
Agarwal, R., 2020. The 5 Classification Evaluation metrics every Data Scientist must know [WWW Document]. Medium. URL https://towardsdatascience.com/the-5-classification-evaluation-metrics-you-must-know-aa97784ff226 (accessed 7.14.21).
Annisa, R., 2019. ANALISIS KOMPARASI ALGORITMA KLASIFIKASI DATA MINING UNTUK PREDIKSI PENDERITA PENYAKIT JANTUNG. JTIK J. Tek. Inform. Kaputama 3, 22–28. https://doi.org/10.59697/jtik.v3i1.650
Bansal, M., Goyal, A., Choudhary, A., 2022. A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning. Decis. Anal. J. 3, 100071. https://doi.org/10.1016/j.dajour.2022.100071
Caron, M., Bojanowski, P., Joulin, A., Douze, M., 2018. Deep Clustering for Unsupervised Learning of Visual Features, in: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (Eds.), Computer Vision – ECCV 2018. Springer International Publishing, Cham, pp. 139–156. https://doi.org/10.1007/978-3-030-01264-9_9
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., Lopez, A., 2020. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing 408, 189–215. https://doi.org/10.1016/j.neucom.2019.10.118
Ennaji, O., Vergütz, L., El Allali, A., 2023. Machine learning in nutrient management: A review. Artif. Intell. Agric. 9, 1–11. https://doi.org/10.1016/j.aiia.2023.06.001
Hanifah, W., Oktavia, W.S., Nisa, H., 2021. FAKTOR GAYA HIDUP DAN PENYAKIT JANTUNG KORONER: REVIEW SISTEMATIK PADA ORANG DEWASA DI INDONESIA. Penelit. Gizi Dan Makanan J. Nutr. Food Res. 44, 45–58. https://doi.org/10.22435/pgm.v44i1.4187
Hidayat, R., 2024. Dataset Heart Desease. https://doi.org/10.5281/ZENODO.13208473
Jimmy, Yulianto, L.D., Hermaliani, E.H., Kurniawati, L., 2023. Penerapan Machine Learning Dalam Analisis Stadium Penyakit Hati Untuk Proses Diagnosis dan Perawatan. Resolusi Rekayasa Tek. Inform. Dan Inf. 3, 144–154. https://doi.org/10.30865/resolusi.v3i4.709
Kaggle, 2019. Heart Disease Dataset [WWW Document]. Heart Dis. Dataset. URL https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset (accessed 8.7.24).
Kemenkes, K., 2023. Cegah Penyakit Jantung dengan Menerapkan Perilaku CERDIK dan PATUH. Sehat Negeriku. URL https://sehatnegeriku.kemkes.go.id/baca/rilis-media/20230925/4943963/cegah-penyakit-jantung-dengan-menerapkan-perilaku-cerdik-dan-patuh/ (accessed 8.5.24).
Kurilová, V., Goga, J., Oravec, M., Pavlovičová, J., Kajan, S., 2021. Support vector machine and deep-learning object detection for localisation of hard exudates. Sci. Rep. 11, 16045. https://doi.org/10.1038/s41598-021-95519-0
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, e0215219. https://doi.org/10.1371/journal.pone.0215219
Muharram, F.R., Multazam, C.E.C.Z., Mustofa, A., Socha, W., Andrianto, Martini, S., Aminde, L., Yi-Li, C., 2024. The 30 Years of Shifting in The Indonesian Cardiovascular Burden—Analysis of The Global Burden of Disease Study. J. Epidemiol. Glob. Health 14, 193–212. https://doi.org/10.1007/s44197-024-00187-8
Muñoz, M.A., Villanova, L., Baatar, D., Smith-Miles, K., 2018. Instance spaces for machine learning classification. Mach. Learn. 107, 109–147. https://doi.org/10.1007/s10994-017-5629-5
Murugan, D.K., Said, Z., Panchal, H., Gupta, N.K., Subramani, S., Kumar, A., Sadasivuni, K.K., 2023. Machine learning approaches for real-time forecasting of solar still distillate output. Environ. Chall. 13, 100779. https://doi.org/10.1016/j.envc.2023.100779
Rahayu, D.C., Hakim, L., Harefa, K., 2021. FAKTOR-FAKTOR YANG MEMPENGARUHI KEJADIAN PENYAKIT JANTUNG KORONER DI RSUD RANTAU PRAPAT TAHUN 2020. PREPOTIF J. Kesehat. Masy. 5, 1055–1057. https://doi.org/10.31004/prepotif.v5i2.2379
Sahar, S., 2020. Analisis Perbandingan Metode K-Nearest Neighbor dan Naïve Bayes Clasiffier Pada Dataset Penderita Penyakit Jantung. Indones. J. Data Sci. 1, 79–86. https://doi.org/10.33096/ijodas.v1i3.20
Simanjorang, R.M., Simangunsong, A., Arifin, M., Yamin, M., 2024. Penerapan Sistem Pakar Dalam Diagnosis Dini Penyakit Jantung Dengan Metode Sistem Inferensi Fuzzy. J. Ilmu Komput. Dan Sist. Inf. JIKOMSI 7, 131–142.
Ula, M., Anjani, F.T.T., Ulva, A.F., Sahputra, I., Pratama, A., 2022. APPLICATION OF MACHINE LEARNING WITH THE BINARY DECISION TREE MODEL IN DETERMINING THE CLASSIFICATION OF DENTAL DISEASE. J. Inform. Telecommun. Eng. 6, 170–179. https://doi.org/10.31289/jite.v6i1.7341
Ureel, Y., Dobbelaere, M.R., Ouyang, Y., De Ras, K., Sabbe, M.K., Marin, G.B., Van Geem, K.M., 2023. Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead! Engineering 27, 23–30. https://doi.org/10.1016/j.eng.2023.02.019
Wahyuni, S.N., 2024. Implementation of Multiple Linear Regression for Predicting Time Series Data in Infectious Diseases Using a Machine Learning Approach. JATISI J. Tek. Inform. Dan Sist. Inf. 11. https://doi.org/10.35957/jatisi.v11i2.7878
WHO, W., 2024. Cardiovascular diseases [WWW Document]. Cardiovasc. Dis. URL https://www.who.int/health-topics/cardiovascular-diseases (accessed 8.5.24).
Published
2024-09-30
How to Cite
Hidayat, R., Sy, Y. S., Sujana, T., Husnah, M., Saputra, H. T., & Okmayura, F. (2024). Implementasi Machine Learning Untuk Prediksi Penyakit Jantung Menggunakan Algoritma Support Vector Machine. BIOS : Jurnal Teknologi Informasi Dan Rekayasa Komputer, 5(2), 161-168. https://doi.org/10.37148/bios.v5i2.152
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