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’s 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.
BackgroundSince lung cancer is the biggest killer in cancer families, extremely threatening to human health, an understanding of social-economic and environmental impacts on lung cancer mortality (LCM) is imperative to improve patient psychological health and potentially mitigate lung cancer incidences and multimorbidity. Figuring out key indicators in social-economic and environment impacts which are sensitive to LCM on spatial-temporal scales contributes to preclinical control and systemic treatments for lung cancer with standard chemotherapy agents are still relatively ineffective. MethodsBased on lung cancer mortalities in 94 countries within a decade (2006-2016), this research appropriately dissects social-economic, demographic, and environmental independent variable effects using Artificial Neural Network (ANN) and CRT Random Decision Tree algorithms (CRT-CRT-RDF).ResultsWith the two methods comparison, the similarity is that education and carbon emission were two etiologies. Education in low and middle countries of lung cancer was related with total ecological footprint and total population. Carbon emission in extreme countries was linked to ecological forestland footprint. Spatial-temporal analysis postulated China and the U.S were the two largest countries whereas China and India were the two fastest countries of LCM growth. Both models have a high precision of prediction (96.1% of CRT-CRT-RDF and 98.4% of ANN).ConclusionsThis research will facilitate preventive lung cancer services, prioritize the geographical allocation of lung cancer investment for WHO, and provide evidence for shrinking carbon emission and deforestation.
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