Prediction of 14-day Hospitalization Risk in Chronic Heart Failure Patients, using Interpretable Machine Learning Methods

Purpose: We wished to investigate whether the risk of acute hospitalizations of chronic heart failure (CHF) patients, could be predicted from biweekly measurements of pulse, blood pressure and weight. We emphasized machine learning models with a high degree of interpretability, due to low adaptation of complex machine learning models in clinical practice. Methods: Using 11,575 measurements of pulse, blood pressure and weight belonging to 122 patients, we trained three types of machine learning algorithms, logistic regression, Random Forest and “RuleFit” to predict nonelective hospitalization within the next 14 days. We used a fivefold cross validation framework to estimate performance metrics, including f-measure, “Receiver Operating Characteristic—Area Under the Curve” (ROC-AUC), sensitivity and specificity. Results: A simple interpretable machine learning algorithm, logistic regression with least absolute shrinkage and selection operator (lasso), performed the best. The regression based on simple features performed with a ROC-AUC of 0.622 (sensitivity = 0.185, specificity = 0.93), while the regression based on a more complex feature set performed with a ROC-AUC of 0.657 (sensitivity = 0.212, specificity = 0.921). Conclusion: In our study simple interpretable methods, outperformed more complex black box machine learning methods in predicting hospitalization of heart failure patients. This suggests that interpretable methods are appropriate in this context. However, the strength of results are slightly limited by the overall modest performance of the models and the small sample size. Clinical Trial Registration: The original trial was registered at ClinicalTrials.gov, with the identification number NCT02860013 at the 9th of august, 2016.