Optimizing Decision Tree Hyperparameters via Random Search for Accurate Heart Failure Risk Prediction
DOI:
https://doi.org/10.54082/jiki.312Keywords:
Decision Tree, Heart Failure Prediction, Hyperparameter Optimization, Machine Learning, Random SearchAbstract
Heart failure remains one of the leading causes of mortality worldwide, highlighting the need for reliable early-detection models to support clinical decision-making. This study investigates the effect of Random Search–based hyperparameter optimization on a Decision Tree model for heart failure risk prediction using a clinical dataset comprising 918 samples and 11 demographic and cardiovascular features. Rather than introducing a novel optimization algorithm, this work focuses on analyzing model performance sensitivity to hyperparameter tuning in a real-world medical dataset. The baseline Decision Tree achieved an accuracy of 0.80. After Random Search optimization, accuracy improved to 0.84, while recall for the positive class increased from 0.83 to 0.90, indicating a notable reduction in false-negative predictions. The optimized configuration, characterized by a shallow tree depth and increased minimum samples per leaf, suggests improved generalization and reduced overfitting. Compared with related studies employing ensemble-based models and genetic optimization, the proposed approach achieves competitive performance using a simpler and more interpretable classifier. These findings demonstrate that systematic hyperparameter tuning can substantially enhance the clinical utility of conventional machine learning models. Practically, the improved recall supports the use of the optimized Decision Tree as a screening-oriented decision support tool, enabling earlier identification of high-risk patients while maintaining model transparency. This study highlights the importance of dataset-specific optimization and provides a foundation for future work involving ensemble methods and advanced optimization strategies to develop robust and clinically applicable heart failure prediction systems.
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