[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123281-en":3,"doc-seo-123281-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123281,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Predicting moderate drinking behaviors in National Health and Nutrition Examination Survey participants using biochemical and demographical factors with machine learning","Recent studies indicate that any alcohol consumption can be detrimental to overall health, yet most research emphasizes problem drinking rather than moderate drinking. Few studies have used machine learning to directly compare the biological health profiles of moderate drinkers with abstainers. Using NHANES participant data with biochemical and demographic variables, prediction models were built with stacked ensembles and gradient boosting to identify the factors most associated with moderate drinking behavior.","UC Irvine  \nUC Irvine Previously Published Works  \nTitle  \nPredicting moderate drinking behaviors in National Health and Nutrition Examination Survey participants using biochemical and demographical factors with machine learning  \nPermalink  \n[https://escholarship.org/uc/item/6001656k](https://escholarship.org/uc/item/6001656k)  \nJournal  \nAlcohol, 113  \nISSN  \n0741-8329  \nAuthors  \nLeaks, Kalan  \nNorden-Krichmar, Trina Brody, James P  \nPublication Date  \n2023-12-01  \nDOI  \n10.1016/j.alcohol.2023.07.005  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nAlcohol 113 (2023) 1e10  \nContents lists available at ScienceDirect  \nAlcohol  \njournal homepage: [http://www. alcoholjournal. org/](http://www. alcoholjournal. org/)  \n| Predicting moderate drinking behaviors in National Health and Nutrition Examination Survey participants using biochemical and demographical factors with machine learning\u003Cbr>Kalan Leaks a, Trina Norden-Krichmar b, James P. Brody a, *\u003Cbr>a Department of Biomedical Engineering, University of California, Irvine 3120 Natural Sciences II, Irvine, CA 92697-2715, United States\u003Cbr>b Department of Epidemiology & Biostatistics, University of California, Irvine, 856 Health Sciences Quad, Suite 3400, Irvine, CA 92617, United States |  |  |  |\n| --- | --- | --- | --- |\n| a r t i c l e i n f o |  | a b s t r a c t |  |\n| Article history:\u003Cbr>Received 29 January 2023\u003Cbr>Received in revised form\u003Cbr>26 June 2023\u003Cbr>Accepted 25 July 2023 |  | Recent studies revealed that any amount of alcohol consumption is an overall health detriment to multiple populations, contrary to popular beliefs. In addition, very few alcohol use studies utilized machine learning methods to compare the biological health of moderate drinkers compared to those that abstain from alcohol consumption, opting instead to focus on binge drinking and heavy drinking. Using participant data of multiple factor types from the National Health and Nutrition Examination Survey, we created prediction models with stacked ensembles and gradient boosting models. Machine learning models were used to identify which factors most enabled the prediction of moderate drinking behaviors. Our combined factor runs produced a cross-validation area under the curve (AUC) of 0.929 and a validation area under the curve of 0.806. Runs that only included biochemical or demographical factors received cross-validation AUC values of 0.825 and 0.925, and validation AUC values of 0.757 and 0.783, respectively. The top predictive factors for our machine learning runs, including gamma glutamyl transferase, gender, iron levels, and cigarette and marijuana usage, corroborate past studies that link those factors to alcohol consumption. Our findings identified key differences in the biological health of moderate drinkers compared to those that abstain from drinking. These results reveal a need to further explore the health effects of moderate drinking, especially for vulnerable populations.\u003Cbr>© 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/) ). |  |\n| Keywords: alcohol data science\u003Cbr>machine learning\u003Cbr>moderate drinking NHANES prediction models |  |  |  |\n\nIntroduction  \nAlthough alcohol consumption is commonplace in the US and globally, most studies focus on and prioritize the health effects of problem drinking, rather than moderate drinking. Of adults in the United States above 18 years old, 85.6% have consumed alcohol at least once in their life. Recent usage statistics show that 69.5% drank alcohol within the past year, and 54.9% drank in the past month (Substance A","cbCaioll7vXCyMC3","https://ap.wps.com/l/cbCaioll7vXCyMC3","pdf",2189873,1,11,"English","en",105,"# Introduction\n## Alcohol consumption patterns and definitions\n# Methods\n## Data source: NHANES\n## Feature sets and model types\n# Results\n## Predictive performance and key factors\n# Discussion\n## Health implications for moderate drinking\n# Conclusion\n## Need for further investigation","[{\"question\":\"Why focus on moderate drinking rather than problem drinking?\",\"answer\":\"Most epidemiological work prioritizes problem drinking outcomes, while evidence on moderate drinking remains inconclusive. The study addresses this gap by modeling moderate drinking behavior directly.\"},{\"question\":\"Which machine learning approaches were used to predict moderate drinking?\",\"answer\":\"The work developed prediction models using stacked ensemble methods and gradient boosting models. These models were evaluated to determine which variables best supported prediction.\"},{\"question\":\"What variables contributed most to predicting moderate drinking behavior?\",\"answer\":\"The top predictive factors included gamma glutamyl transferase, gender, iron levels, and cigarette and marijuana usage, aligning with prior findings linking these factors to alcohol consumption.\"}]","Predicting moderate drinking behaviors in National Health and Nutrition Examination Survey participants using biochemical and demographical factors with machine learning | PDF",1785815724,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-moderate-drinking-behaviors-in-national-health-and-nutrition-examination-survey-participants-using-biochemical-and-demographical-factors-with-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-moderate-drinking-behaviors-in-national-health-and-nutrition-examination-survey-participants-using-biochemical-and-demographical-factors-with-machine-learning/123281/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why focus on moderate drinking rather than problem drinking?","Question",{"text":75,"@type":76},"Most epidemiological work prioritizes problem drinking outcomes, while evidence on moderate drinking remains inconclusive. The study addresses this gap by modeling moderate drinking behavior directly.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning approaches were used to predict moderate drinking?",{"text":80,"@type":76},"The work developed prediction models using stacked ensemble methods and gradient boosting models. These models were evaluated to determine which variables best supported prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"What variables contributed most to predicting moderate drinking behavior?",{"text":84,"@type":76},"The top predictive factors included gamma glutamyl transferase, gender, iron levels, and cigarette and marijuana usage, aligning with prior findings linking these factors to alcohol consumption.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]