ArticleBioMed research international2020
Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.Computational and mathematical methods in medicine · 2022Pooled it
- A systematic review of multimodal machine learning models for heart failure classification and prognosis prediction.Frontiers in cardiovascular medicine · 2026Review
- Unsupervised machine learning for cardiovascular disease: A framework for future studies.European journal of heart failure · 2025Review
- Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review.Journal of the American Heart Association · 2025Review
- Time-Adaptive Machine Learning Models for Predicting the Severity of Heart Failure with Reduced Ejection Fraction.Diagnostics (Basel, Switzerland) · 2025Article
- Efficacy of AI Models in Detecting Heart Failure Using ECG Data: A Systematic Review and Meta-Analysis.Cureus · 2025Review
- Article
- Time-domain heart rate variability features for automatic congestive heart failure prediction.ESC heart failure · 2024Article
- Artificial Intelligence in Heart Failure: Friend or Foe?Life (Basel, Switzerland) · 2024Review
- Heart failure survival prediction using novel transfer learning based probabilistic features.PeerJ. Computer science · 2024Article
- A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis.Journal of geriatric cardiology : JGC · 2022Article
- Characterization of English Braille Patterns Using Automated Tools and RICA Based Feature Extraction Methods.Sensors (Basel, Switzerland) · 2022Article
- Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique.Frontiers in big data · 2022Article
- A Study on the Association between Korotkoff Sound Signaling and Chronic Heart Failure (CHF) Based on Computer-Assisted Diagnoses.Journal of healthcare engineering · 2022Article
- A Machine Learning Approach for Chronic Heart Failure Diagnosis.Diagnostics (Basel, Switzerland) · 2021Article
- Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning.Frontiers in physiology · 2021Article
- Explainable Machine Learning for COVID-19 Pneumonia Classification With Texture-Based Features Extraction in Chest Radiography.Frontiers in digital health · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI [lower bound = 0.04, upper bound = 0.89]), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI [lower bound 0.07, upper bound = 0.81]) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI [lower bound = 0.07, upper bound = 0.86]). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure 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 OpenQuestion graph.