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All Journal Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Djoko Rahardjo
Universitas Bumigora, Mataram, Indonesia
Computer Science & IT

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A Comparison of Logistic Regression, Random Forest, and XGBoost Based on Feature Importance in Heart Failure Prediction M. Thoriq Panca Mukti; Hairani Hairani; Djoko Rahardjo; M. Rizki
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 5 No. 2 (2026): September 2026 (In Press)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v5i2.6583

Abstract

Heart failure is a cardiovascular disease with a high mortality rate, requiring a prediction system capable of assisting in faster and more accurate early detection. This study aims to compare the performance of Logistic Regression, Random Forest, and XGBoost in predicting heart failure, with a focus on feature importance. The dataset is a public Kaggle dataset, consisting of 918 patient records with 11 features and 1 target attribute. The research stages include exploratory data analysis (EDA), data preprocessing, anomaly handling, label encoding, data standardization, model training, model evaluation, and feature importance analysis. Model evaluation was conducted using accuracy, precision, recall, and F1-score. The results indicate that Random Forest achieved the best performance, with an accuracy of 86.96%, a precision and recall of 88.24%, and an F1-score of 88.24%. Meanwhile, XGBoost achieved an accuracy of 85.87%, and Logistic Regression achieved 84.78%. The feature importance analysis revealed that the ST_Slope attribute was the most dominant feature across all three models in predicting heart failure. This study demonstrates that the Random Forest method provides superior classification performance compared to the other models, and feature importance analysis can aid in interpreting the clinical attributes that influence heart failure prediction.
A Comparative Analysis of Machine Learning Models Using PCA and Variance Threshold Approaches for Classifying Student Academic Success Djoko Rahardjo; Hairani Hairani; M. Thoriq Panca Mukti; M. Rizki
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 5 No. 2 (2026): September 2026 (In Press)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v5i2.6585

Abstract

Predicting student academic success is an important problem in higher education due to the high risk of delayed graduation or dropout. However, the prediction process often faces challenges, such as an imbalanced class distribution, which can reduce the classification model’s performance. This study aims to evaluate and compare the performance of Logistic Regression, Random Forest, and Gradient Boosting algorithms in classifying student academic status into Graduate, Enrolled, and Dropout categories. The research method begins with data balancing using the Synthetic Minority Oversampling Technique (SMOTE) applied to the training data, followed by a comparison of two dimensionality reduction techniques: feature selection with Variance Threshold (VT) and feature extraction with Principal Component Analysis (PCA). Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results show that Random Forest without dimensionality reduction achieves the best performance, with an accuracy of 77.40% on SMOTE-balanced data. The application of SMOTE has also been shown to improve the balance of predictive capability across classes, as evidenced by higher F1-scores than with the original data, which is biased toward the majority class. Conversely, the use of PCA degrades model performance by reducing the information available to differentiate the classes. Furthermore, the Enrolled class is the most difficult to predict due to its high similarity in characteristics to the Graduate and Dropout classes. Based on these results, the combination of Random Forest and SMOTE, without dimensionality reduction, is the most effective approach for predicting students’ academic success. This model has the potential to be implemented as a component of an Early Warning System to support the identification of at-risk students and the implementation of earlier academic interventions.  
Co-Authors Hairani Hairani M. Rizki M. Thoriq Panca Mukti