ArticleDiagnostics (Basel, Switzerland)2021
A Machine Learning Approach for Chronic Heart Failure Diagnosis.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Heart rate variability as a dual-use digital biomarker: integrating clinical, AI, and operational perspectives on human performance and resilience.BMC cardiovascular disorders · 2026Pooled it
- CardiaTics: An explainable AI integrated heart disease diagnosis model with feature engineering and stacked ensemble approach.Journal of big data · 2026Article
- Time-Adaptive Machine Learning Models for Predicting the Severity of Heart Failure with Reduced Ejection Fraction.Diagnostics (Basel, Switzerland) · 2025Article
- An explainable multi-objective hybrid machine learning model for reducing heart failure mortality.PeerJ. Computer science · 2025Article
- Leveraging fuzzy embedded wavelet neural network with multi-criteria decision-making approach for coronary artery disease prediction using biomedical data.Scientific reports · 2024Article
- Predictive Analytics in Heart Failure Risk, Readmission, and Mortality Prediction: A Review.Cureus · 2024Review
- Article
- An Integrated Machine Learning Approach for Congestive Heart Failure Prediction.Diagnostics (Basel, Switzerland) · 2024Article
- A novel ternary pattern-based automatic psychiatric disorders classification using ECG signals.Cognitive neurodynamics · 2024Article
- Assessing myocardial indices and inflammatory factors to determine anxiety and depression severity in patients with chronic heart failure.World journal of psychiatry · 2024Article
- Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases.Diagnostics (Basel, Switzerland) · 2024Article
- Heart failure survival prediction using novel transfer learning based probabilistic features.PeerJ. Computer science · 2024Article
- BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients.Scientific reports · 2023Article
- An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit.Diagnostics (Basel, Switzerland) · 2022Article
- Left Ventricular Pressure Estimation Using Machine Learning-Based Heart Sound Classification.Frontiers in cardiovascular medicine · 2022Article
- An Efficient Machine Learning Model Based on Improved Features Selections for Early and Accurate Heart Disease Predication.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
The aim of this study was to address chronic heart failure (HF) diagnosis with the application of machine learning (ML) approaches. In the present study, we simulated the procedure that is followed in clinical practice, as the models we built are based on various combinations of feature categories, e.g., clinical features, echocardiogram, and laboratory findings. We also investigated the incremental value of each feature type. The total number of subjects utilized was 422. An ML approach is proposed, comprising of feature selection, handling class imbalance, and classification steps. The results for HF diagnosis were quite satisfactory with a high accuracy (91.23%), sensitivity (93.83%), and specificity (89.62%) when features from all categories were utilized. The results remained quite high, even in cases where single feature types were employed.
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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 OpenQuestion graph.