KLASIFIKASI DATA MINING UNTUK PREDIKSI PENYAKIT KARDIOVASKULAR

  • Nia Nuraeni Universitas Nusa Mandiri

Abstract

According to the World Health Organization (WHO), cardiovascular diseases are one of the leading causes of death worldwide. Cardiovascular diseases involve heart and blood vessel conditions that commonly occur in communities. These conditions encompass various diseases such as coronary heart disease, heart failure, stroke, and peripheral vascular disease. Major risk factors include high blood pressure, high cholesterol, and smoking. Premature deaths due to heart diseases can be prevented by controlling the risk factors and identifying individuals at high risk of developing such diseases. One of the most effective ways to identify and predict heart diseases is through the use of data mining algorithms. Data mining algorithms can address issues in diagnosing cardiovascular or heart diseases by utilizing predictive models, such as Decision Tree, Naive Bayes, Logistic Regression, K-Nearest Neighbor, and others. In this study, identification was performed using classification algorithms including Naïve Bayes, Logistic Regression, Decision Tree Classifier, k-NN, SVM, XGBoost, and Random Forest. The highest accuracy, reaching 98%, was obtained from the Random Forest algorithm

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Published
2024-06-30
How to Cite
NURAENI, Nia. KLASIFIKASI DATA MINING UNTUK PREDIKSI PENYAKIT KARDIOVASKULAR. Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 7, n. 1, p. 161-170, june 2024. ISSN 2621-3079. Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1276>. Date accessed: 19 july 2024. doi: https://doi.org/10.37600/tekinkom.v7i1.1276.
Section
Articles