Predicting Cardiac Arrest Early: A Deep Learning Approach with Statistical Models

Pravallika.Miriyala; Mr. Yerrabathana Guravaiah

Predicting Cardiac Arrest Early: A Deep Learning Approach with Statistical Models

Authors: Pravallika.Miriyala, Mr. Yerrabathana Guravaiah

Unique Paper ID: 177416
Volume: 11
Issue: 12
PageNo: 561-565

Keywords: Cardiac Arrest ML DL Dataset

Abstract:
Cardiac arrest is a life-threatening condition that requires immediate intervention to prevent mortality. Despite advances in medical monitoring, early prediction remains a complex task due to the non-linear and multivariate nature of physiological data. This research presents a hybrid approach combining deep learning with traditional statistical models to predict cardiac arrest in clinical settings. Using real-time patient data such as ECG, blood pressure, heart rate, oxygen saturation, and lab reports, our system employs LSTM-based neural networks for temporal analysis and logistic regression for interpretability and feature importance. The integration enhances prediction accuracy while maintaining clinical transparency. Our results demonstrate a significant improvement in early warning times, potentially enabling healthcare providers to act proactively and save lives.. The proposed model will be used in the CICU to enable early detection of cardiac arrest in newborn babies. In a training (Tr) comparison region, the proposed CMLA reached 0.912 delta-p value, 0.894 False discovery rate (FDR) value, 0.076 False omission rate (FOR) value, 0.859 prevalence threshold value, and 0.842 CSI value. In a testing (Ts) comparison region, the proposed CMLA reached 0.896 delta-p values, 0.878 FDR value, 0.061 FOR value, 0.844 prevalence threshold values, and 0.827 CSI value. It will help reduce the mortality and morbidity of newborn babies due to cardiac arrest in the CICU.

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ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 561-565

Predicting Cardiac Arrest Early: A Deep Learning Approach with Statistical Models

Available:https://ijirt.org/Article?manuscript=177416

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