A Comparative Analysis of Hyper-Parameter Optimization Methods for Predicting Heart Failure Outcomes

This study presents a comparative analysis of hyper-parameter optimization methods used in developing predictive models for patients at risk of heart failure readmission and mortality. We evaluated three optimization approaches—Grid Search (GS), Random Search (RS), and Bayesian Search (BS)—across three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). The models were built using real patient data from the Zigong Fourth People’s Hospital, which included 167 features from 2008 patients. The mean, MICE, kNN, and RF imputation techniques were implemented to handle missing values. Our initial results showed that SVM models outperformed the others, achieving an accuracy of up to 0.6294, sensitivity above 0.61, and an AUC score exceeding 0.66. However, after 10-fold cross-validation, the RF models demonstrated superior robustness, with an average AUC improvement of 0.03815, whereas the SVM models showed potential for overfitting, with a slight decline (−0.0074). The XGBoost models exhibited moderate improvement (+0.01683) post-validation. Bayesian Search had the best computational efficiency, consistently requiring less processing time than the Grid and Random Search methods. This study reveals that while model selection is crucial, an appropriate optimization method and imputation technique significantly impact model performance. These findings provide valuable insights for developing robust predictive models for healthcare applications, particularly for heart failure risk assessment.