Key result
Random Forest model predicts cardiac arrest with 92% accuracy, outperforming logistic regression and SVM.
Why the study?
Early detection of cardiac arrest remains a significant challenge due to the nonlinear and complex behavior of physiological signals.
Do machine learning models using continuous vital signs accurately predict cardiac arrest in real time?
Do machine learning models using continuous vital signs accurately predict cardiac arrest in real time?
A Random Forest machine learning model using continuous vital signs can predict cardiac arrest with 92% accuracy, offering potential for integration into hospital and wearable monitoring systems.
May improve early CA detection but should not yet alter monitoring; leaves open prospective real-world validation.
Cardiac arrest (CA) is an acute and life-threatening condition presenting with the sudden loss of cardiac activity, leading to immediate cessation of blood flow to central organs and loss of consciousness. In spite of the significant advances in emergency medical services and ongoing patient monitoring, early detection of cardiac arrest is still a significant challenge because of the nonlinear and complex behavior of physiological signals. This study introduces a strong data-driven machine learning (ML) model for predicting cardiac arrest in real time based on continuous tracking of vital signs like heart rate variability, blood pressure, ECG signal variation, and oxygen saturation levels (SpO₂). Three supervised learning modelsLogistic Regression, Support Vector Machine (SVM), and Random Forestwere created and contrasted following normalization and feature selection. Of these, Random Forest model showed the best predictive results with 92% accuracy, 90% precision, and 91% recall. The developed system has strong prospects for integration into hospital monitoring devices and wearable technology, facilitating timely intervention and better patient survival rates.
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Navya et al. (2026) studied Cardiac arrest. Random Forest machine learning model vs. Logistic Regression and Support Vector Machine (SVM) was evaluated on Prediction of cardiac arrest. A Random Forest machine learning model predicted cardiac arrest with 92% accuracy, 90% precision, and 91% recall, outperforming Logistic Regression and SVM models.
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