Comparing Machine Learning Classifiers for Predicting Hospital Readmission of Heart Failure Patients in Rwanda

Abstract

High rates of hospital readmission and the cost of treating heart failure (HF) are significant public health issues globally and in Rwanda. Using machine learning (ML) to predict which patients are at high risk for HF hospital readmission 20 days after their discharge has the potential to improve HF management by enabling early interventions and individualized treatment approaches. In this paper, we compared six different ML models for this task, including multi-layer perceptron (MLP), K-nearest neighbors (KNN), logistic regression (LR), decision trees (DT), random forests (RF), and support vector machines (SVM) with both linear and radial basis kernels. The outputs of the classifiers are compared using performance metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. We found that RF outperforms all the remaining models with an AUC of 94% while SVM, MLP, and KNN all yield 88% AUC. In contrast, DT performs poorly, with an AUC value of 57%. Hence, hospitals in Rwanda can benefit from using the RF classifier to determine which HF patients are at high risk of hospital readmission.

Predictive Analytics in Heart Failure Prediction: A Systematic Review

“…Data Cleaning [7], [13], [19], [20], [23], [26], [28], [32], [33], [34], [35], [36], [39], [40], [42], [41], [45], [46], [47], [54], [55], [60], [64], [65], [66], [68], [69] Mean imputation, predictive mean matching, median imputation, random forest imputation, kNN imputation, XGBoost imputation, and missForest…”

“…Data Transformation [13], [14], [22], [23], [24], [25], [26], [30], [31], [34], [36], [39], [41], [42], [43], [47], [54], [55], [58], [60], [62], [33], [63], [64], [65], [69] Recursive feature elminiation, SelectKBest, Chi-Square, Pearson's correlation, KS-Test, T-Test Data Reduction [47], [50], [54] PCA Data Balancing [13], [14], [21], [22], [23], [32], [33], [39], [43], [55], [62], [65], [66] SMOTE, Under-sampling, Oversampling, ADASYSN Data cleaning includes removing missing values from the dataset and performing imputation techniques to impute the missing values. The mean imputation frequently appears in various studies, while the machine learning-based imputation technique obtained better results than statistical-based imputation.…”

“…From the medical records of other countries, [33] implemented machine learning algorithms in the building of their predictive models to predict the risk of hospitalization for patients with heart failure in Rwanda. This study involved the medical records of seven hospitals and was done between 2018 and 2019.…”