Key result
Decision tree model predicts congestive heart failure with ~81% accuracy on test data.
Why the study?
Congestive heart failure is a leading cause of morbidity and mortality worldwide influenced by multiple clinical risk factors, motivating the development of an interpretable machine learning framework for risk prediction.
Does a decision tree machine learning model improve interpretable congestive heart failure risk prediction compared to random forest, gradient boosted trees, and naïve Bayes models in patients from public heart disease repositories?
Observational (n=4,927)
Yes
Does a decision tree machine learning model improve interpretable congestive heart failure risk prediction compared to random forest, gradient boosted trees, and naïve Bayes models in patients from public heart disease repositories?
A decision tree machine learning model integrated with a business intelligence dashboard achieved 80.58% accuracy and 77.00% recall in classifying congestive heart failure from public datasets while providing rule-based interpretability.
AI models may enhance CHF risk prediction from routine data; leaves open prospective validation before clinical use.
Congestive heart failure (CHF) is a leading cause of morbidity and mortality worldwide and is influenced by multiple clinical risk factors, including age, sex, blood pressure, cholesterol levels, and electrocardiographic characteristics. This study employs publicly available heart disease datasets compiled from populations in Cleveland, Hungary, Switzerland, and Long Beach repositories (2015–2020) and analyzes 11 predictor attributes and one binary outcome using the cross-industry standard process for data mining (CRISP-DM) framework. Four supervised machine learning (ML) models—decision tree, random forest, gradient boosted trees, and naïve Bayes—were developed and evaluated using confusion matrix–based performance metrics, with results showing that the decision tree model offers balanced predictive performance and clear interpretability, making it suitable for heart failure risk prediction. The findings demonstrate the potential of ML approaches to support clinical risk assessment and enhance decision-making in heart failure management.
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Tangprasert et al. (2026) conducted an observational in Congestive heart failure (n=4,927). Decision tree machine learning model vs. Random forest, gradient boosted tree, and naïve Bayes models was evaluated on Classification accuracy for congestive heart failure risk prediction on the held-out test set. The decision tree model predicted congestive heart failure with 80.58% accuracy, 68.18% precision, 77.00% recall, and a 72.32% F-measure on the 1,478-case test set.
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