2Ai-Enabled Clinical Decision Support for Cardiac Event Prediction

Abstract

Cardiac Event will be appeared through
long distance of disease by cardiovascular
diseases (CVDs) are a leading cause of
morbidity and mortality worldwide. Early
detection of cardiac events such as
myocardial infarction and arrhythmia
significantly improves clinical outcomes.
Traditional diagnostic methods rely on
manual interpretation of
electrocardiograms (ECGs),
echocardiograms, and patient risk
factors, which can be time consuming
and subject to human error. This project
proposes an AI driven predictive
diagnostic system for early detection of
cardiac events. The system integrates
machine learning with clinical data, ECG
signals, and patient history to identify
high risk individuals. Deep learning
models, including recurrent neural
networks (RNNs) and convolutional
neural networks (CNNs), analyze
temporal ECG patterns and