ArticleDiagnostics (Basel, Switzerland)2023
Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 31 citations in OpenAlex.
- Enhanced Prediction of Cardiovascular Disease Through Integrated Machine Learning Models Combining Clinical and Demographic Characteristics.Diagnostics (Basel, Switzerland) · 2026Article
- Integrated machine learning and multi-dataset analysis identify SDS as a diagnostic and immune-related hub gene in hepatoblastoma.Frontiers in oncology · 2026Article
- Review
- Predicting coronary heart disease with advanced machine learning classifiers for improved cardiovascular risk assessment.Scientific reports · 2025Article
- Identification of signature genes and subtypes for heart failure diagnosis based on machine learning.Frontiers in cardiovascular medicine · 2025Article
- From Data to Decision: Interpretable and Reliable Heart Disease Risk Prediction.Current health sciences journalArticle
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient's heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%.
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