Analysis of the Effect of SMOTE on the Performance of Random Forest in Classifying the Risk of Death in Heart Failure Patients
Year: 2026
Type: article
Abstract: Class distribution imbalance in the heart failure patient dataset has the potential to affect the ability of the classification model to identify patients experiencing death events. This study aims to analyze the effect of the Synthetic Minority Over-sampling Technique (SMOTE) application on the performance of the Random Forest algorithm in classifying death events in heart failure patients. The dataset used is the Heart Failure Clinical Records Dataset consisting of 299 patient data, with 12 predictor attributes and one target attribute, namely DEATH EVENT. Data processing was carried out using the Python programming language through the Google Colab platform. The research stages include data preparation, dataset division with a ratio of 80:20, application of SMOTE to training data, formation of a Random Forest model, and performance evaluation using accuracy, precision, recall, and F1-score metrics. In addition, Stratified 5-Fold Cross-Validation was applied as an additional evaluation to analyze model performance on several data divisions. The test results with an 80:20 data split showed that the model before and after applying SMOTE produced an accuracy value of 83.33%, a positive class precision of 80.00%, a recall of 63.16%, and an F1-score of 70.59%. Meanwhile, the results of Stratified 5-Fold Cross-Validation showed that the application of SMOTE increased the average recall value from 69.74% to 76.00% and the F1-score from 73.34% to 74.75%. However, the average accuracy value decreased from 83.93% to 83.60%, while the precision decreased from 77.93% to 73.74%. These findings indicate that the application of SMOTE has the potential to improve the ability of Random Forest in identifying patients with the label DEATH EVENT = 1, although it does not improve all evaluation metrics. This study is expected to provide an empirical contribution in evaluating the effectiveness of SMOTE in classifying imbalanced medical data, especially in improving the detection ability of minority classes.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7225066473