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.
Insights
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.
Background:
Coronary artery disease (CAD) is recognized as the leading cause of death worldwide. This study analyses CAD risk factors using an artificial neural network (ANN) to predict CAD.
Methods:
The research data were obtained from a multi-center study, namely the Iran-premature coronary artery disease (I-PAD). The current study used the medical records of 415 patients with CAD hospitalized in Razi Hospital, Birjand, Iran, between May 2016 and June 2019. A total of 43 variables that affect CAD were selected, and the relevant data was extracted. Once the data were cleaned and normalized, they were imported into SPSS (V26) for analysis. The present study used the ANN technique.
Results:
The study revealed that 48% of the study population had a history of CAD, including 9.4% with premature CAD and 38.8% with CAD. The variables of age, sex, occupation, smoking, opium use, pesticide exposure, anxiety, sexual activity, and high fasting blood sugar were found to be significantly different among the three groups of CAD, premature CAD, and non-CAD individuals. The neural network achieved success with five hidden fitted layers and an accuracy of 81% in non-CAD diagnosis, 79% in premature diagnosis, and 78% in CAD diagnosis. Anxiety, acceptance, eduction and gender were the four most important factors in the ANN model.
Conclusions:
The current study shows that anxiety is a high-prevalence risk factor for CAD in the hospitalized population. There is a need to implement measures to increase awareness about the psychological factors that can be managed in individuals at high risk for future CAD.
