Heart Failure Readmission or Early Death Risk Factor Analysis: A Case Study in a Telemonitoring Program

Artetxe, Arkaitz; Larburu, Nekane; Murga, Nekane; Escolar, Vanessa; Graña, Manuel

doi:10.1007/978-3-319-59397-5_26

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…They found that SVM had better performance than other methods. Similar results were also reported in a study conducted by Artetxe et al [ 25 ]. In another study, Awan et al [ 17 ] used different ML methods to predict 30-day readmission or death with an imbalanced dataset.…”

Section: Discussionsupporting

confidence: 92%

Predicting Hospital Readmission in Heart Failure Patients in Iran: A Comparison of Various Machine Learning Methods

et al. 2021

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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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Section: Discussionsupporting

confidence: 92%

Predicting Hospital Readmission in Heart Failure Patients in Iran: A Comparison of Various Machine Learning Methods

et al. 2021

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“…37 Perhaps one of the reasons why CAT performed better in our study and in some previous studies was this. 39,40 In our study, the most important predictors for readmission, 1-month mortality, and 1-year mortality were, respectively, length of stay in the hospital, hemoglobin level, and family history of MI. In the previous study, 41 there was a relationship between the length of stay in the hospital and readmission.…”

Section: Discussionmentioning

confidence: 52%

“…The neural networks (NN) model had the best predictive performance with an AUC of 0.764 and 0.674 in the test and external validation cohort, respectively 25 . Landicho et al and Artetxe et al reported that the SVM had better performance in predicting the readmission of HF patients than other algorithms 39,40 …”

Section: Discussionmentioning

confidence: 99%

Predicting the risk of mortality and rehospitalization in heart failure patients: A retrospective cohort study by machine learning approach

Ketabi,

Andishgar,

Fereidouni

et al. 2024

Clinical Cardiology

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BackgroundHeart failure (HF) is a global problem, affecting more than 26 million people worldwide. This study evaluated the performance of 10 machine learning (ML) algorithms and chose the best algorithm to predict mortality and readmission of HF patients by using The Fasa Registry on Systolic HF (FaRSH) database.HypothesisML algorithms may better identify patients at increased risk of HF readmission or death with demographic and clinical data.MethodsThrough comprehensive evaluation, the best‐performing model was used for prediction. Finally, all the trained models were applied to the test data, which included 20% of the total data. For the final evaluation and comparison of the models, five metrics were used: accuracy, F1‐score, sensitivity, specificity and Area Under Curve (AUC).ResultsTen ML algorithms were evaluated. The CatBoost (CAT) algorithm uses a series of decision tree models to create a nonlinear model, and this CAT algorithm performed the best of the 10 models studied. According to the three final outcomes from this study, which involved 2488 participants, 366 (14.7%) of the patients were readmitted to the hospital, 97 (3.9%) of the patients died within 1 month of the follow‐up, and 342 (13.7%) of the patients died within 1 year of the follow‐up. The most significant variables to predict the events were length of stay in the hospital, hemoglobin level, and family history of MI.ConclusionsThe ML‐based risk stratification tool was able to assess the risk of 5‐year all‐cause mortality and readmission in patients with HF. ML could provide an explicit explanation of individualized risk prediction and give physicians an intuitive understanding of the influence of critical features in the model.

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Heart Failure Readmission or Early Death Risk Factor Analysis: A Case Study in a Telemonitoring Program

Cited by 2 publications

References 13 publications

Predicting Hospital Readmission in Heart Failure Patients in Iran: A Comparison of Various Machine Learning Methods

Predicting Hospital Readmission in Heart Failure Patients in Iran: A Comparison of Various Machine Learning Methods

Predicting the risk of mortality and rehospitalization in heart failure patients: A retrospective cohort study by machine learning approach

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