Key result
The Random Forest machine learning method achieved the highest accuracy (0.90-0.91) for predicting hospital readmission in heart failure patients compared to other evaluated models.
Why the study?
Heart failure is a common disease with a high readmission rate, and medical data often suffer from class imbalance and missing values.
Which machine learning method performs best for predicting hospital readmission in heart failure patients?
Cohort (n=1,856)
No
Which machine learning method performs best for predicting hospital readmission in heart failure patients?
Random forest machine learning models can accurately predict hospital readmission in heart failure patients, outperforming other common algorithms.
ML models may aid HF readmission stratification; leaves open whether they improve outcomes before prospective validation.
OBJECTIVES: Heart failure (HF) is a common disease with a high hospital readmission rate. This study considered class imbalance and missing data, which are two common issues in medical data. The current study's main goal was to compare the performance of six machine learning (ML) methods for predicting hospital readmission in HF patients. METHODS: In this retrospective cohort study, information of 1,856 HF patients was analyzed. These patients were hospitalized in Farshchian Heart Center in Hamadan Province in Western Iran, from October 2015 to July 2019. The support vector machine (SVM), least-square SVM (LS-SVM), bagging, random forest (RF), AdaBoost, and naïve Bayes (NB) methods were used to predict hospital readmission. These methods' performance was evaluated using sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Two imputation methods were also used to deal with missing data. RESULTS: Of the 1,856 HF patients, 29.9% had at least one hospital readmission. Among the ML methods, LS-SVM performed the worst, with accuracy in the range of 0.57-0.60, while RF performed the best, with the highest accuracy (range, 0.90-0.91). Other ML methods showed relatively good performance, with accuracy exceeding 0.84 in the test datasets. Furthermore, the performance of the SVM and LS-SVM methods in terms of accuracy was higher with the multiple imputation method than with the median imputation method. CONCLUSIONS: This study showed that RF performed better, in terms of accuracy, than other methods for predicting hospital readmission in HF patients.
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Najafi‐Vosough et al. (2021) conducted a cohort in Heart failure (n=1,856). Random Forest machine learning model vs. Other machine learning models (SVM, LS-SVM, bagging, AdaBoost, NB) was evaluated on Accuracy for predicting hospital readmission. The Random Forest machine learning method achieved the highest accuracy (0.90-0.91) for predicting hospital readmission in heart failure patients compared to other evaluated models.
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