Prediction of Cardiac Arrest

This project focuses on predicting the likelihood of cardiac arrest by using machine learning models trained on relevant health data. Four different algorithms were employed, including Random Forest, and the Random Forest model showed the best performance in terms of accuracy and prediction quality.

The goal is to help healthcare professionals identify at-risk patients more effectively, enabling timely interventions. This data-driven approach leverages advanced algorithms to enhance medical decision-making, improving patient outcomes in cardiac care.

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