Background:
Young mortality is prevalent among patients with cardiovascular disease (CVD). To develop prediction models for CVD mortality in young adults, it is crucial to assess CVD risks. Early detection of cardiac disorders using machine learning algorithms, a branch of artificial intelligence (AI) is crucial for preventing more damage to coronary arteries and saving lives.

Aims:
To predict mortality versus a life outcome among young adults (18-45 years) with CVD using AI technique known as Chi-squared automatic interaction detector (CHAID) algorithms.

Methods:
A large-scale dataset was extracted from the electronic health records of 809 young adult patients diagnosed with CVD using a retrospective design. Information was retrieved regarding young adults from Jordan who were admitted to public health institutions between 2015 and the end of 2021.

Results:
CHAID algorithms were chosen among seven prediction models based on accuracy and area under curve to predict mortality vs life in young individuals (18-45 years old) with CVD. The mortality prediction algorithms started with pulse pressure, then diastolic blood pressure, then ischemic heart disease, and last geographical location.

Conclusions:
CHAID model used in our study indicated how the death rate was classified and distributed among a variety of parameters. As a result, we may argue that AI model could provide additional information on how many aspects are articulated in connection to CVD patient fatality situations.