IMPLEMENTASI METODE SVM UNTUK MEMPREDIKSI RISIKO PENYAKIT JANTUNG PADA LANSIA
Bilah Samping Artikel
Isi Artikel Utama
Abstrak
Heart disease is one of the main causes of death in elderly people throughout the world. Early detection of the risk of heart disease is very important to prevent more severe complications. This research aims to develop a heart disease risk prediction system in the elderly using the Support Vector Machine (SVM) method. The SVM method was chosen because of its ability to classify non-linear data effectively and accurately. The dataset used includes elderly patient data with various attributes such as age, blood pressure, cholesterol, heart rate and disease history. After going through the data pre-processing process, the model is trained and tested using the SVM algorithm. The test results show that the model is able to achieve an accuracy level of 93.55% with a precision value of 0.94, recall of 0.94, and f1-score of 0.93 (weighted average). With these data results, this system has the potential to become a tool in the initial screening process for the risk of heart disease in the elderly by medical personnel.
Heart disease is one of the main causes of death in elderly people throughout the world. Early detection of the risk of heart disease is very important to prevent more severe complications. This research aims to develop a heart disease risk prediction system in the elderly using the Support Vector Machine (SVM) method. The SVM method was chosen because of its ability to classify non-linear data effectively and accurately. The dataset used includes elderly patient data with various attributes such as age, blood pressure, cholesterol, heart rate and disease history. After going through the data pre-processing process, the model is trained and tested using the SVM algorithm. The test results show that the model is able to achieve an accuracy level of 93.55% with a precision value of 0.94, recall of 0.94, and f1-score of 0.93 (weighted average). With these data results, this system has the potential to become a tool in the initial screening process for the risk of heart disease in the elderly by medical personnel.
Rincian Artikel
Referensi
World Health Organization, “Cardiovascular diseases (CVDs),” 2021. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
Kementerian Kesehatan RI, “Infodatin: Penyakit Jantung,” Pusat Data dan Informasi, 2020. [Online]. Available: https://pusdatin.kemkes.go.id
I. Rahmawati dan H. A. Nugroho, “Risiko Penyakit Jantung pada Lansia: Faktor dan Pencegahan,” Jurnal Kesehatan Masyarakat, vol. 8, no. 2, pp. 140–147, 2020.
A. Supriyanto, R. H. Putra, dan A. F. Harahap, “Sistem Prediksi Penyakit Jantung Menggunakan Metode Klasifikasi,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 5, no. 1, pp. 1–6, 2022.
S. Yadav, R. Patel, dan R. Sharma, “Heart Disease Prediction using Hybrid Model,” Procedia Computer Science, vol. 167, pp. 1960–1968, 2020.
J. Han, M. Kamber, dan J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Waltham, MA: Morgan Kaufmann, 2012.
C. Cortes dan V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. [Online]. Available: https://doi.org/10.1007/BF00994018
R. Dey, S. Chakraborty, dan A. Mukherjee, “Heart Disease Prediction Model Using SVM and Logistic Regression,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 9, no. 4, pp. 2981–2988, 2021.
G. Ramesh dan B. V. Kavitha, “Prediction of Heart Disease Using Support Vector Machine with Optimization Techniques,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 5, no. 2, pp. 63–70, 2020.
F. Soriano, “Heart Failure Prediction Dataset,” Kaggle, 2022. [Online]. Available: https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction
A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, 2nd ed. Sebastopol, CA: O’Reilly Media, 2019.
S. B. Kotsiantis, D. Kanellopoulos, dan P. E. Pintelas, “Data Preprocessing for Supervised Learning,” International Journal of Computer Science, vol. 1, no. 2, pp. 111–117, 2006.
A. Géron, “Data Preprocessing – Label Encoding & One-Hot Encoding,” Hands-On Machine Learning, 2nd ed., O’Reilly, 2019, ch. 2.
A. Géron, “Evaluating Classifiers: Accuracy, Precision, Recall,” Hands-On Machine Learning, 2nd ed., O’Reilly, 2019, ch. 3.
R. Abdu, I. Kurniawan, dan A. Rahmadani, “Implementasi Algoritma Support Vector Machine (SVM) untuk Klasifikasi Citra,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 10, no. 1, pp. 20–26, 2020.
M. Bansal, K. Kumari, dan R. K. Sehgal, “Comparative Analysis of SVM and CNN for Image Classification,” International Journal of Engineering and Advanced Technology (IJEAT), vol. 11, no. 3, pp. 88–92, 2022.
D. M. W. Powers, “Evaluation: From Precision, Recall and F-measure to ROC, Informedness, Markedness & Correlation,” Journal of Machine Learning Technologies, vol. 2, no. 1, pp. 37–63, 2011.
Streamlit, “Streamlit Docs,” Streamlit.io, 2023. [Online]. Available: https://docs.streamlit.io