Machine learning driven early prediction of cardiac arrest

Abstract

Background

Cardiac Arrest (CA) is a major cause of mortality globally, often occurring suddenly without prior warning, making early detection and timely intervention crucial to saving lives. Traditional methods of predicting CA have proven inadequate due to the lack of clear warning signs. With the integration of Machine Learning (ML) techniques, the potential for more accurate early detection and intervention can improve survival rates.

Objective

This study proposes a machine learning-based approach for the early prediction of Cardiac Vascular Disease (CVD), which is a primary contributor to CA. The model incorporates various patient data, including lab results, vital signs, and Electrocardiogram (ECG) signal readings, to enhance prediction accuracy.

Methods

The study employs a range of advanced machine learning techniques, including Gradient-Boosting Algorithm (GBA), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN). To process the data, Wavelet Transform (WT) is used to decompose the ECG signals, isolating important features while minimizing noise. Feature selection is performed through an innovative Modified Recursive Feature Elimination (MRFE) technique.

Results

The machine learning models were validated using the MATLAB simulator, with evaluation metrics including accuracy, precision, recall, and F-score. Among the models, ANN demonstrated the highest performance, achieving 96.3% accuracy, 96.1% precision, 95% recall, and 94.65% F-score.

Conclusion

This work demonstrates the effectiveness of machine learning in the early prediction of CA, enabling timely medical intervention and potentially saving lives. The results suggest that the proposed model could become a valuable tool for healthcare professionals in managing and preventing cardiac arrest.