Forskning · Mar 2026
Heart Disease Prediction: A Comparative Study of Machine Learning Pipelines
Developed 200 machine learning pipelines for heart disease prediction using CDC health data.
Developed a comprehensive machine learning pipeline for predicting heart disease from CDC health survey data. Evaluated 200 unique combinations of datasets, feature selection methods, sampling techniques, and ML models. Compared Decision Trees, Logistic Regression, Neural Networks (MLP), and XGBoost classifiers. Addressed severe class imbalance using SMOTE, Borderline-SMOTE, ADASYN, and SMOTE-ENN. Experimented with multiple feature selection strategies, including Chi-Square, manual selection, and near-constant feature removal. Used stratified 10-fold cross-validation to ensure reliable and comparable model evaluation. Focused on Recall, F1-score, and ROC-AUC due to the importance of detecting minority-class heart disease cases. Analyzed both the 2022 CDC dataset and a combined 2020–2022 dataset containing hundreds of thousands of records. XGBoost combined with SMOTE-ENN achieved the strongest overall F1-score of 0.467 on the 2022 dataset. The project demonstrated how preprocessing and class-imbalance handling can significantly affect machine learning performance in healthcare applications.
- Machine Learning
- SMOTE
- Python