AI-Enhanced Neural Network Framework for Cardiac Arrest Prognostics

Abstract

The research paper consists on a framework incorporating AI and ANNs to improve the predictive performance in Cardiac Arrest (CA) treatment. The proposed system combines artificial neural network analysis with clinical data to forecast the likelihood of Cardiac arrest so that preventive measures can be taken. Our approach involves creating a multilayer perceptron ANN with input data containing the patient's characteristics, real-time vital signs, and medical history. The proposed model's performance is assessed based on the following parameters: accuracy, sensitivity, specificity, and the area under the ROC curve. The outcomes show that the attained prediction performance is higher than typical statistical-based techniques, illustrating the value of AI techniques in the critical care context. In addition, the paper reviews how to improve the interpretability of ANN models by performing feature importance analysis to arrive at clinically relevant and valuable predictions. Incorporating this advanced AI framework into current healthcare models should go a long way toward changing how cardiac arrests are treated through early identification and individualised approaches. In conclusion, this research shows that AI and neural networks are essential in improving healthcare. The future holds great promise for further development of predictive analytics and patient care optimisations.