Risk and demographic factors examination models for lung cancer incidences in the United States

Jiayu Chen

doi:10.1117/12.2669548

24 March 2023 Risk and demographic factors examination models for lung cancer incidences in the United States

Jiayu Chen

Proceedings Volume 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022); 126115D (2023) https://doi.org/10.1117/12.2669548
Event: International Conference on Biological Engineering and Medical Science (ICBioMed2022), 2022, Oxford, United Kingdom

Abstract

Lung cancer has been the leading cause of cancer deaths worldwide since the spread of smoking and the decline in air quality. This paper analyzes the risk factors and demographic variables that contribute to lung cancer incidences in the United States. Risk factors include smoking and environmental pollution, while demographic variables include education, income level, age group, and ethnicity. Multiple linear regression model and random forest model are used for variable interpretation and lung cancer prediction. In the model evaluation, the multiple linear regression model is found with better prediction accuracy than the random forest model by using cross-validation with resampling. The random forest model has 6.82, 0.70, and 5.21 of RMSE, R squared, and MAE, while the multiple linear regression model has 5.70, 0.74, and 4.70. Smoking is the most contributing risk factor based on the result, and the most significant demographic variables are Hispanic and high school education proportions. The discussion section includes the interpretation of the groups of variables, potential limitations in the models, and the future direction of finding deeper correlations between lung cancer incidences and the selected risk and demographic variables. This analysis illustrates the importance of demographic variables in cancer and shows more directions for research on demographic variables.

Citation Download Citation

Jiayu Chen "Risk and demographic factors examination models for lung cancer incidences in the United States", Proc. SPIE 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022), 126115D (24 March 2023); https://doi.org/10.1117/12.2669548

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KEYWORDS

Lung cancer

Random forests

Cancer

Linear regression

Tumor growth modeling

Air quality

Air contamination

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