Academic Journal
Enhanced Prediction of Cardiac Risk in Neonates Using Calibrated Ensemble Learning Approaches.
| Τίτλος: | Enhanced Prediction of Cardiac Risk in Neonates Using Calibrated Ensemble Learning Approaches. |
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| Συγγραφείς: | Naresh VS; Department of CSE, Sri Vasavi Engineering College, Tadepalligudem, Andhra Pradesh, India. vsnaresh111@gmail.com., Vineela M; Department of CSE, Sri Vasavi Engineering College, Tadepalligudem, Andhra Pradesh, India. |
| Πηγή: | Pediatric cardiology [Pediatr Cardiol] 2026 Oct; Vol. 47 (7), pp. 3043-3059. Date of Electronic Publication: 2025 Nov 12. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Springer Verlag Country of Publication: United States NLM ID: 8003849 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1971 (Electronic) Linking ISSN: 01720643 NLM ISO Abbreviation: Pediatr Cardiol Subsets: MEDLINE |
| Imprint Name(s): | Publication: New York Ny : Springer Verlag Original Publication: [New York, Springer-Verlag] |
| Ιατρικοί όροι (MeSH): | Heart Arrest*/diagnosis , Machine Learning*, Risk Assessment/methods ; Humans ; Infant, Newborn ; Random Forest ; Boosting Machine Learning Algorithms ; Predictive Learning Models ; Classification Algorithms ; Prediction Algorithms ; Multilayer Perceptrons |
| Περίληψη: | This study proposes a Heterogeneous Hybrid Machine Learning Model (HHMM) for the early detection of cardiac arrest in newborns in intensive care. The model combines three classifiers through stacking: XGBoost (XGB), Random Forest (RF), and Multilayer Perceptron (MLP), where their probability outputs serve as meta-features. The Random Forest classifier acts as a meta-learner, aggregating predictions for the final classification. The weighted stacking method assigns performance-based weights to the base classifiers by combining their probabilities using weighted averaging. The HHMM was trained and tested using clinical cardiovascular risk data. The model performance was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results showed that HHMM outperformed the individual classifiers and conventional ensembles, achieving 95% accuracy, 96% precision, 94% recall, and 95% AUC-ROC. The HHMM's performance stems from its ability to capture complex patterns, reduce overfitting, and enhance generalization. The interpretability of the model through probability calibration makes it suitable for use in clinical decision-support systems. This study advances cardiac risk prediction in neonates using calibrated ensemble learning and stacking, thereby providing a foundation for real-time monitoring systems to improve patient outcomes in neonatal-care. (© 2025. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.) |
| Competing Interests: | Declarations. Conflict of interest: The authors declare that they have no affiliations with or involvement in any organization or entity with any financial interest in the subject matter or materials discussed in this manuscript. Ethical Approval: Not Applicable. |
| References: | Artif Intell Med. 1997 Nov;11(3):215-39. (PMID: 9413607) Inflamm Res. 2025 May 31;74(1):88. (PMID: 40448718) Comput Biol Med. 2021 Aug;135:104554. (PMID: 34139440) JMIR Med Inform. 2024 Jul 23;12:e49142. (PMID: 39051152) Artif Intell Med. 2022 Jun;128:102289. (PMID: 35534143) Diagnostics (Basel). 2022 Dec 18;12(12):. (PMID: 36553222) J Am Heart Assoc. 2018 Jun 26;7(13):. (PMID: 29945914) |
| Contributed Indexing: | Keywords: Cardiac arrest prediction; Early detection; Ensemble learning; Neonatal intensive care unit; Probability calibration; Stacking |
| Entry Date(s): | Date Created: 20251112 Date Completed: 20260922 Latest Revision: 20261008 |
| Update Code: | 20261008 |
| DOI: | 10.1007/s00246-025-04091-1 |
| PMID: | 41222642 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1432-1971 |
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| DOI: | 10.1007/s00246-025-04091-1 |