Using machine learning models to predict coronary artery calcium scores in firefighters

Abstract

Objective: To develop and compare the predictive accuracy of machine learning (ML) models for coronary artery calcium (CAC) prediction among firefighters and to evaluate their cross-validated performance against traditional binary logistic regression (BLR). Methods: This study utilized health records from 416 firefighters who underwent comprehensive health screenings at Ascension Public Safety Medical. CAC was assessed using cardiac computed tomography scans. The degree of CAC was measured using the Agatston scores. 17 clinical and lifestyle related risk variables were collected. Machine learning models, including XGBoost, Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and K Nearest Neighbor (KNN), were developed and compared. Additionally, the performance of these ML models was evaluated against traditional binary logistic regression (BLR). Results: Among the 416 firefighters, age (r = 0.28, p < 0.0001), glucose levels (r = 0.13, p = 0.001), monocyte percentages (r = 0.13, p = 0.001), and resting systolic blood pressure (r = 0.13, p = 0.009) were positively associated with CAC. While sodium levels (r = -0.11, p = 0.038), GFR (r = -0.17, p = 0.021), and maximum oxygen volumes (r = -0.19, p = 0.0002) were inversely associated with CAC. XGBoost achieved the highest cross-validated area under the curve (AUC) of 0.770, outperforming NB (0.768), SVM (0.765), RF (0.749), KNN (0.671), and BLR (0.658). Conclusion: Our research demonstrates the efficacy of ML algorithms, particularly XGBoost, in enhancing early detection and preventive strategies for CAC among firefighters. These advancements are crucial for proactive health management in this high-risk group, potentially mitigating risks associated with their demanding profession.