Studi Komparatif Model Klasifikasi Kerentanan Penyakit Jantung Menggunakan Algoritma Machine Learning
Abstract
Penyakit jantung merupakan salah satu penyebab kematian baik di dunia maupun Indonesia. Perhatian awal dari penyakit jantung akan memudahkan pencegahan dan penyembuhanya. Tujuan penelitian ini adalah melakukan analisis komapratif model klasifikasi dengan berbagai algoritma machine learning untuk kerentanan penyakit jantung. Dataset diambil dari UCI machine Learning Resipatory dengan 300 data training dan 100 data testing. Parameter klasifikasi terdiri dari age, sex, systolic blood pressure, cholesterol, thalach, oldpeak dan slope, serta labelnya cardio. Model klasifikasi dibangun dengan algoritma Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree, random Forest, Backpropagation, Logistic Regression dan Support Vector machine (SVM). Hasil model klasifikasi dari pengukuran accuracy didapatkan Naïve Bayes (79,00%), KNN (63,00%), Decision Tree (66,00%), Random Forest (77,00%), Backpropagation (80,00%), Logistic Regression (81,00%) dan SVM (80,00%). Dari analisis komparatif pegukuran parameter accuracy, precision, recall dan F1 score maka model klasifikasi dengan algoritma Logistic Regression dan backpropagation menghasilkan performa terbaik.
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