{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T21:45:54Z","timestamp":1789076754106,"version":"build-2803163510"},"reference-count":45,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T00:00:00Z","timestamp":1667001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Lung cancer remains the leading cause for cancer mortality worldwide. While it is well-known that smoking is an avoidable high-risk factor for lung cancer, it is necessary to identify the extent to which other modified risk factors might further affect the cell\u2019s genetic predisposition for lung cancer susceptibility, and the spreading of carcinogens in various geographical zones. This study aims to examine the association between lung cancer mortality (LCM) and major risk factors. We used Fuzzy Inference Modeling (FIM) and Random Forest Modeling (RFM) approaches to analyze LCM and its possible links to 30 risk factors in 100 countries over the period from 2006 to 2016. Analysis results suggest that in addition to smoking, low physical activity, child wasting, low birth weight due to short gestation, iron deficiency, diet low in nuts and seeds, vitamin A deficiency, low bone mineral density, air pollution, and a diet high in sodium are potential risk factors associated with LCM. This study demonstrates the usefulness of two approaches for multi-factor analysis of determining risk factors associated with cancer mortality.<\/jats:p>","DOI":"10.3390\/ijerph192114161","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T04:57:34Z","timestamp":1667105854000},"page":"14161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Determining Association between Lung Cancer Mortality Worldwide and Risk Factors Using Fuzzy Inference Modeling and Random Forest Modeling"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8032-3317","authenticated-orcid":false,"given":"Xiu","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Geography and Environmental Studies, Texas State University, San Marcos, TX 78666, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4021-1534","authenticated-orcid":false,"given":"Blanchard-Boehm","family":"Denise","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Studies, Texas State University, San Marcos, TX 78666, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F.","family":"Zhan","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Studies, Texas State University, San Marcos, TX 78666, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3037-7645","authenticated-orcid":false,"given":"Jinting","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Science, Wuhan University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"key":"ref_1","first-page":"927","article-title":"Chapter 28\u2014Respiratory disease","volume":"22","author":"White","year":"2021","journal-title":"Kumar Clark\u2019s Clin. 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