ArticleHealthcare informatics research2021
Predicting Hospital Readmission in Heart Failure Patients in Iran: A Comparison of Various Machine Learning Methods.
Article in Healthcare informatics research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.BMC medical informatics and decision making · 2026Pooled it
- Digital therapeutics into geriatric cardiovascular emergency care.Frontiers in digital health · 2026Review
- Machine learning-based evaluation of prognostic factors for mortality and relapse in patients with acute lymphoblastic leukemia: a comparative simulation study.BMC medical informatics and decision making · 2024Article
- The predictive power of data: machine learning analysis for Covid-19 mortality based on personal, clinical, preclinical, and laboratory variables in a case-control study.BMC infectious diseases · 2024Article
- Predicting the risk of mortality and rehospitalization in heart failure patients: A retrospective cohort study by machine learning approach.Clinical cardiology · 2024Article
- Longitudinal machine learning model for predicting systolic blood pressure in patients with heart failure.Journal of preventive medicine and hygiene · 2023Article
- Predicting polypharmacy in half a million adults in the Iranian population: comparison of machine learning algorithms.BMC medical informatics and decision making · 2023Article
- Predictive modeling for COVID-19 readmission risk using machine learning algorithms.BMC medical informatics and decision making · 2022Article
- Predicting hospital readmission risk in patients with COVID-19: A machine learning approach.Informatics in medicine unlocked · 2022Article
- Prediction of Hepatitis disease using ensemble learning methods.Journal of preventive medicine and hygiene · 2022Article
- Adaptation of Interdisciplinary Clinical Practice Guidelines to Palliative Care for Patients with Heart Failure in Iran: Application of Adapte Method.Iranian journal of nursing and midwifery researchArticle
- Predicting readmission rates in critically ill heart failure patients during a 90-day vulnerable phase using interpretable machine learning models.Clinics (Sao Paulo, Brazil)Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesHeart 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.
methodsIn 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.
resultsOf 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.
conclusionsThis study showed that RF performed better, in terms of accuracy, than other methods for predicting hospital readmission in HF patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.