Cardiac Arrest Prediction Using Machine Learning: Random Forest And Svm Framework For Early Detection

Authors: T.Naga Navya, V.Naga Harshitha, K.Manisha, K.Gayathri, K. Rishitha

Abstract: 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.

DOI: https://doi.org/10.5281/zenodo.20063385