{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T20:12:13Z","timestamp":1760904733015,"version":"build-2065373602"},"reference-count":39,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Informatics J"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:p>\n            <jats:bold>Objective:<\/jats:bold>\n            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).\n            <jats:bold>Methods:<\/jats:bold>\n            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), Na\u00efve 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).\n            <jats:bold>Results:<\/jats:bold>\n            Among the 416 firefighters, age (r = 0.28,\n            <jats:italic toggle=\"yes\">p<\/jats:italic>\n            &lt; 0.0001), glucose levels (r = 0.13,\n            <jats:italic toggle=\"yes\">p<\/jats:italic>\n            = 0.001), monocyte percentages (r = 0.13,\n            <jats:italic toggle=\"yes\">p<\/jats:italic>\n            = 0.001), and resting systolic blood pressure (r = 0.13,\n            <jats:italic toggle=\"yes\">p<\/jats:italic>\n            = 0.009) were positively associated with CAC. While sodium levels (r = \u22120.11,\n            <jats:italic toggle=\"yes\">p<\/jats:italic>\n            = 0.038), GFR (r = \u22120.17,\n            <jats:italic toggle=\"yes\">p<\/jats:italic>\n            = 0.021), and maximum oxygen volumes (r = \u22120.19,\n            <jats:italic toggle=\"yes\">p<\/jats:italic>\n            = 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).\n            <jats:bold>Conclusion:<\/jats:bold>\n            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.\n          <\/jats:p>","DOI":"10.1177\/14604582251381274","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T07:23:12Z","timestamp":1760685792000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Using machine learning models to predict coronary artery calcium scores in firefighters"],"prefix":"10.1177","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0907-0358","authenticated-orcid":false,"given":"Mingyue","family":"Li","sequence":"first","affiliation":[{"name":"Indiana University"},{"name":"University of Texas Southwestern Medical Center"}]},{"given":"Jiali","family":"Han","sequence":"additional","affiliation":[{"name":"Indiana University"},{"name":"Indiana University Melvin and Bren Simon Comprehensive Cancer Center"}]},{"given":"Carolyn","family":"Muegge","sequence":"additional","affiliation":[{"name":"National Institute for Public Safety Health"},{"name":"University of Indianapolis"}]},{"given":"Terrell","family":"Zollinger","sequence":"additional","affiliation":[{"name":"Indiana University"}]},{"given":"Yixi","family":"Xu","sequence":"additional","affiliation":[{"name":"Indiana University School of Medicine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2848-2104","authenticated-orcid":false,"given":"Laura Y","family":"Zhou","sequence":"additional","affiliation":[{"name":"Indiana University School of Medicine"}]},{"given":"Patrick","family":"Monahan","sequence":"additional","affiliation":[{"name":"Indiana University School of Medicine"}]},{"given":"Jennifer","family":"Wessel","sequence":"additional","affiliation":[{"name":"Indiana University"}]},{"given":"Vanessa","family":"Kleinschmidt","sequence":"additional","affiliation":[{"name":"National Institute for Public Safety Health"}]},{"given":"Steven","family":"Moffatt","sequence":"additional","affiliation":[{"name":"National Institute for Public Safety Health"}]},{"given":"Hongmei","family":"Nan","sequence":"additional","affiliation":[{"name":"Indiana University"}]}],"member":"179","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"e_1_3_8_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/toxics12030201"},{"key":"e_1_3_8_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-48161-1_19"},{"key":"e_1_3_8_4_2","doi-asserted-by":"publisher","DOI":"10.1093\/occmed\/kqs116"},{"key":"e_1_3_8_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cjtee.2021.06.001"},{"key":"e_1_3_8_6_2","doi-asserted-by":"publisher","DOI":"10.1097\/JOM.0000000000002057"},{"key":"e_1_3_8_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pcad.2021.09.001"},{"key":"e_1_3_8_8_2","volume-title":"Firefighter Health: A Narrative Review of Occupational Threats and Countermeasures. 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