The Relationship between Lung Cancer Prevalence and Air Quality and Other Factors

Abstract

Lung cancer poses a significant global health threat. This study employs machine learning techniques for in-depth analysis of a lung cancer patient dataset. Five models were developed, and following a thorough exploration of variable relationships, the study utilized KNN, SVM, and multiple logistic regression methods to construct these models. The accuracy metric served as a performance measure, ultimately identifying the multiple logistic regression model as the most effective, boasting an accuracy of 0.9655. The findings of the multiple logistic regression model revealed that for every one-unit increase in smoking level, the risk of severe lung cancer increased. The study's results emphasize the importance of residing in areas with good air quality and reducing smoking levels to mitigate the risk of severe lung cancer. But none of the models are perfect predictors of lung cancer severity. Therefore, it is necessary to establish more comprehensive clinical data and complex analytical techniques to improve diagnostic performance. This model lays a foundation for better judging the probability of lung cancer by data-driven prediction.