Perbandingan Decision Tree dan Neural Network dalam Prediksi LVEF pada Pasien Gagal Jantung - Eva Rahmawati; Mieke Nurmalasari; Hosizah Markam; Dhiar Niken Larasati (Jurnal IT UHB, 2026)
Heart failure is a cardiovascular disease with a high mortality rate, affecting more than 64 million people worldwide, with a one-year fatality rate of 33%. To assess cardiac performance, the Left Ventricular Ejection Fraction (LVEF) indicator was used, which reflects the ability of the left ventricle to pump blood. Therefore, an analytical approach is needed to predict LVEF values more accurately based on patient characteristics. This study aimed to compare the performance of Decision Tree and Neural Network algorithms in predicting LVEF values in patients with heart failure. Data processing was conducted using Orange Data Mining, utilizing risk factor variables as the basis for classification. The approach used was supervised learning, involving 381 heart failure patient records that were split using an 80:20 train-test split technique, resulting in 305 training data and 76 testing data. Class imbalance in the training data was handled using the SMOTE method before the modeling process. The experimental results show that the model built using the Neural Network algorithm provides better performance than the Decision Tree. This was indicated by an AUC value of 64.8%, classification accuracy of 57.9%, F1-score of 55.8%, precision of 57.6%, and recall of 57.9%. In addition, based on the confusion matrix evaluation, the Neural Network algorithm was able to achieve higher accuracy and recall levels in most LVEF categories. Based on these results, it can be concluded that the Neural Network is a more effective method for predicting LVEF values in patients with heart failure. This model is considered more capable of recognizing complex data patterns, thereby producing better predictive accuracy compared with the Decision Tree algorithm.