ArticlePloS one2019
Feature selection and transformation by machine learning reduce variable numbers and improve prediction for heart failure readmission or death.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Deep Learning-Based Prediction of Heart Failure Readmission Using Longitudinal Cardiac Device Data.Journal of cardiovascular electrophysiology · 2026Article
- Comparison of Feature Selection Methods in Machine Learning Models of Cancer Information Seeking Among United States Adults: Cross-Sectional Study.JMIR medical informatics · 2026Article
- Artificial intelligence in heart failure.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Review
- Establishment and validation of a machine learning-based predictive model for in-hospital mortality risk in acute myocardial infarction patients complicated with diabetes mellitus.Frontiers in cardiovascular medicine · 2026Article
- Predictive Performance of Machine Learning Models for Heart Failure Readmission: A Systematic Review.Biomedicines · 2025Review
- Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.International journal of molecular sciences · 2025Article
- Development and validation of a risk prediction model for 30-day readmission in elderly type 2 diabetes patients complicated with heart failure: a multicenter, retrospective study.Frontiers in endocrinology · 2025Article
- Feature Selection and Machine Learning Approaches in Prediction of Current E-Cigarette Use Among U.S. Adults in 2022.International journal of environmental research and public health · 2024Article
- Predictive Analytics in Heart Failure Risk, Readmission, and Mortality Prediction: A Review.Cureus · 2024Review
- Machine Learning-Based Prediction of Binge Drinking among Adults in the United State: Analysis of the 2022 Health Information National Trends Survey.Proceedings of the 2024 9th International Conference on Mathematics and Artificial Intelligence · 2024Article
- An Interpretable Approach with Explainable AI for Heart Stroke Prediction.Diagnostics (Basel, Switzerland) · 2024Article
- A machine learning model to predict heart failure readmission: toward optimal feature set.Frontiers in artificial intelligence · 2024Article
- Artificial intelligence with temporal features outperforms machine learning in predicting diabetes.PLOS digital health · 2023Article
- Improved performance of machine learning models in predicting length of stay, discharge disposition, and inpatient mortality after total knee arthroplasty using patient-specific variables.Arthroplasty (London, England) · 2023Article
- Current challenges in adopting machine learning to critical care and emergency medicine.Clinical and experimental emergency medicine · 2023Article
- Predicting health insurance uptake in Kenya using Random Forest: An analysis of socio-economic and demographic factors.PloS one · 2023Article
- Virtual healthcare solutions in heart failure: a literature review.Frontiers in cardiovascular medicine · 2023Review
- Drug response prediction using graph representation learning and Laplacian feature selection.BMC bioinformatics · 2022Article
- Current state of artificial intelligence-based algorithms for hospital admission prediction in patients with heart failure: a scoping reviewEuropean heart journal. Digital health · 2022Article
- Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases.Frontiers in cardiovascular medicine · 2022Article
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6 authors.
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Abstract
backgroundThe prediction of readmission or death after a hospital discharge for heart failure (HF) remains a major challenge. Modern healthcare systems, electronic health records, and machine learning (ML) techniques allow us to mine data to select the most significant variables (allowing for reduction in the number of variables) without compromising the performance of models used for prediction of readmission and death. Moreover, ML methods based on transformation of variables may potentially further improve the performance.
objectiveTo use ML techniques to determine the most relevant and also transform variables for the prediction of 30-day readmission or death in HF patients.
methodsWe identified all Western Australian patients aged 65 years and above admitted for HF between 2003-2008 in linked administrative data. We evaluated variables associated with HF readmission or death using standard statistical and ML based selection techniques. We also tested the new variables produced by transformation of the original variables. We developed multi-layer perceptron prediction models and compared their predictive performance using metrics such as Area Under the receiver operating characteristic Curve (AUC), sensitivity and specificity.
resultsFollowing hospital discharge, the proportion of 30-day readmissions or death was 23.7% in our cohort of 10,757 HF patients. The prediction model developed by us using a smaller set of variables (n = 8) had comparable performance (AUC 0.62) to the traditional model (n = 47, AUC 0.62). Transformation of the original 47 variables further improved (p<0.001) the performance of the predictive model (AUC 0.66).
conclusionsA small set of variables selected using ML matched the performance of the model that used the full set of 47 variables for predicting 30-day readmission or death in HF patients. Model performance can be further significantly improved by transforming the original variables using ML methods.
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