Which risk factor best predicts coronary artery disease using artificial neural network method?
Nahid Azdaki1,2, Fatemeh Salmani3, Toba Kazemi1
1Cardiovascular Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran.
BMC Medical Informatics and Decision Making
|February 14, 2024
Summary
Anxiety is a significant risk factor for coronary artery disease (CAD). This study used an artificial neural network (ANN) to identify CAD predictors, finding anxiety, education, and gender crucial for risk assessment.
Area of Science:
- Cardiology
- Artificial Intelligence
- Public Health
Background:
- Coronary artery disease (CAD) remains the leading global cause of mortality.
- Identifying and predicting CAD risk factors is crucial for preventative strategies.
Purpose of the Study:
- To analyze coronary artery disease (CAD) risk factors using an artificial neural network (ANN).
- To predict the likelihood of developing CAD in a patient population.
Main Methods:
- Utilized data from 415 patients from the Iran-premature coronary artery disease (I-PAD) study.
- Applied an artificial neural network (ANN) model to 43 selected CAD-related variables.
- Data preprocessing included cleaning and normalization before analysis in SPSS (V26).
Main Results:
- The ANN model achieved diagnostic accuracies of 81% for non-CAD, 79% for premature CAD, and 78% for CAD.
- Key predictors identified by the ANN model were anxiety, acceptance, education, and gender.
- Significant differences in risk factors including age, sex, occupation, smoking, and anxiety were observed across CAD groups.
Conclusions:
- Anxiety is identified as a prevalent risk factor for coronary artery disease (CAD) in hospitalized individuals.
- Highlights the importance of psychological factors, such as anxiety, in CAD risk management.
- Recommends implementing awareness programs for psychological factors impacting CAD risk.
