Analysis of the factor associated with lung cancer based on machine learning

Chuhan Zhang

doi:10.1117/12.2660634

6 December 2022 Analysis of the factor associated with lung cancer based on machine learning

Chuhan Zhang

Proceedings Volume 12458, International Conference on Biomedical and Intelligent Systems (IC-BIS 2022); 124583X (2022) https://doi.org/10.1117/12.2660634
Event: International Conference on Biomedical and Intelligent Systems, 2022, Chengdu, China

Abstract

Contemporarily, lung cancer remains a critical medical issue worldwide, where the survival rate has increased by a dramatic extent from 13% to 22.6%. On this basis, it is necessary to analysis the impacts of factors on the lung cancer in curing the disease. In this paper, the influencing factors that affect the lung cancer are investigated based on the state-of-art machine learning scenarios. Generally, it is feasible to build the prediction model in terms of machine learning approaches to find out the causality of lung cancer. In this case, five models (i.e., Linear regression, random forest classifier, decision tree classifier, boosting classifier, and XGB classifier) are implemented in this research to evaluate the factors based on Python Scikit-learn package and panada package. According to the evaluations, the random forest is the best model among the five models. The precision of the model is 100%, while it might have overfitting. Therefore, it is necessary to increase the sample size and reduce the model complication by applying outer example test. Based on the estimations, the importance of features on lung cancer is evaluated, which offers a guideline for preventing lung cancer and reduce the rate of mortality and incidence of the disease. These results shed light on guiding further exploration to address the issue of curing lung cancer.

Citation Download Citation

Chuhan Zhang "Analysis of the factor associated with lung cancer based on machine learning", Proc. SPIE 12458, International Conference on Biomedical and Intelligent Systems (IC-BIS 2022), 124583X (6 December 2022); https://doi.org/10.1117/12.2660634

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