Determining Association between Lung Cancer Mortality Worldwide and Risk Factors Using Fuzzy Inference Modeling and Random Forest Modeling
Topics:
Keywords: lung cancer mortality-non-smoking factors, Random Forest Tree, Fuzzy Inference Modelling
Abstract Type: Virtual Paper Abstract
Authors:
Xiu Wu, Texas State Unversity
F. Benjamin Zhan, Texas State Unversity
Blanchard-Boehm Denise, Texas State University
Jinting Zhang, Wuhan University
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Abstract
Lung cancer remains the leading cause for cancer mortality worldwide. While it is wellknown 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 the multi-factor analysis of
determining risk factors associated with cancer mortality.
Determining Association between Lung Cancer Mortality Worldwide and Risk Factors Using Fuzzy Inference Modeling and Random Forest Modeling
Category
Virtual Paper Abstract
