Previously submitted to: JMIR Cardio (no longer under consideration since Apr 18, 2024)
Date Submitted: Apr 11, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Cardiovascular disease-related mortality in young adults: Artificial Intelligence prediction Background: Many Cardiovascular disease (CVD) patients die young. Assessing CVD risks is essential to creating prediction models that accurately predict CVD mortality overall and in subgroups like young adults. Machine Learning Algorithms (MLA), a subfield of Artificial Intelligence (AI), must be used to detect heart diseases early to prevent further coronary artery damage and save lives. Objectives: To predict death versus a life outcome among young adults (18-45 years) with CVD using the Chi-squared Automatic Interaction Detector (CHAID) algorithms, an artificial intelligence (AI) technique. Methods: A retrospective design for retrieving larg
ABSTRACT
Background:
: Many Cardiovascular disease (CVD) patients die young. Assessing CVD risks is essential to creating prediction models that accurately predict CVD mortality overall and in subgroups like young adults. Machine Learning Algorithms (MLA), a subfield of Artificial Intelligence (AI), must be used to detect heart diseases early to prevent further coronary artery damage and save lives.
Objective:
To predict death versus a life outcome among young adults (18-45 years) with CVD using the Chi-squared Automatic Interaction Detector (CHAID) algorithms, an artificial intelligence (AI) technique.
Methods:
A retrospective design for retrieving large-scale data from the electronic health records (EHRs) for 809 young adult patients with CVD was used. Data were collected for Jordanian young adults who were admitted to public health hospitals from the year 2015 to the end of 2021.
Results:
Out of seven prediction models created and based on the accuracy level and area under curve, the CHAID algorithm was the selected model for predicting death versus life among young adults (18-45 years old) with CVD. The sequence of prediction algorithms for death was first for the pulse pressure (PP), then the diastolic blood pressure (DBP), then the ischemic heart disease (IHD), ended with the geographical location.
Conclusions:
As demonstrated by the CHAID model used in our study, the mortality rate was categorized and distributed among numerous factors. Therefore, we could assert that the AI model could add more detail on how various factors are articulated in relation to CVD patient death cases.
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