Development and Validation of a Class Imbalance-Resilient Cardiac Arrest Prediction Framework Incorporating Multiscale Aggregation, ICA and Explainability

Development and Validation of a Class Imbalance-Resilient Cardiac Arrest Prediction Framework Incorporating Multiscale Aggregation, ICA and Explainability

<p dir="ltr">Objective: Despite advancements in artificial intelligence (AI) for predicting cardiac arrest (CA) with multivariate time-series vital signs data, existing models continue to face significant problems, particularly concerning balance, efficiency, accuracy, and explainabi...

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Bibliographic Details
Main Author: Iffa Afsa C M (22304104) (author)
Other Authors: Mohammed Yusuf Ansari (16904523) (author), Santu Paul (7145237) (author), Osama Halabi (14158905) (author), Ezzedin Alataresh (22304107) (author), Jassim Shah (18015227) (author), Afaf Hamze (22304110) (author), Omar Aboumarzouk (18427923) (author), Abdulla Al-Ansari (14150583) (author), Sarada Prasad Dakua (14151789) (author)
Published: 2025
Subjects:
Biomedical and clinical sciences
Cardiovascular medicine and haematology
Engineering
Biomedical engineering
Health sciences
Health services and systems
Artificial intelligence
cardiac arrest
explainable AI
independent component analysis
multi-scale temporal aggregation
Predictive models
Feature extraction
Data models
Accuracy
Computational modeling
Cardiac arrest
Biological system modeling
Analytical models
Radio frequency
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