Early prediction of hemodialysis complications employing ensemble techniques

Abstract

Background and objectives Hemodialysis complications remain a critical threat among dialysis patients. They result in sudden termination of the session which impacts the efficiency of dialysis. As intra-dialytic complications are the result of the interplay of multiple factors, artificial intelligence can aid in their early prediction. This research aims to compare different machine learning tools for the early prediction of the most frequent hemodialysis complications with high performance, using the fewest predictors for easier practical implementation. Methods Fifty different variables were recorded during 6000 hemodialysis sessions performed in a regional dialysis unit in Egypt. The filter technique was used to extract the most relevant features. Then, five individual classifiers and three ensemble approaches were implemented to predict the occurrence of intra-dialytic complications. Different subsets of 25, 12 and 6 from the 50 collected features were tested. Results Random forest yielded the highest accuracy of 98% with the least training time using 12 features in a balanced dataset, while the gradient boosting allowed obtaining the highest F1-score of 94%, 92%, and 78% in the prediction of hypotension, hypertension, and dyspnea, respectively, in imbalanced datasets. Conclusion Applying different machine learning algorithms to big datasets can improve accuracy, reduce training time and model complexity allowing simple implementation in clinical practice. Our models can help nephrologists predict and possibly prevent dialysis complications.

Construction and Application of Machine Learning Models for Predicting Intradialytic Hypotension

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Predicting hypoproteinemia among patients undergoing maintenance hemodialysis: A development and validation study based on machine learning algorithms

“…Although the algorithm can be used to check the in uence of input variables on output variables, it cannot provide a clear explanation of collinear variables, and as a consequence, the prediction accuracy of the established models is limited. Some scholars have also tried to apply machine learning algorithms to predict the mortality [20] and complications [21,22] of hemodialysis patients, but hypoproteinemia has not been analyzed as an independent outcome indicator.…”

Deep Learning on Electrocardiograms for Prediction of In-hospital Intradialytic Hypotension in Patients with ESKD

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