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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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Iffa Afsa C M (22304104) (author)
مؤلفون آخرون:	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)
منشور في:	2025
الموضوعات:	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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Development and Validation of a Class Imbalance-Resilient Cardiac Arrest Prediction Framework Incorporating Multiscale Aggregation, ICA and Explainability

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