A clinically acceptable ordinal logistic model to determine risk factors associated with lung cancer levels

The goal of this study is to identify the risk factors that are linked to different stages of lung cancer and to create a model using ordinal logistic regression to predict the likelihood of various cancer stages based on these risk factors. The study found that factors such as air pollution, alcohol use, dust allergy, occupational hazards, genetic risk, chronic lung disease, balanced diet, obesity, passive smoking, chest pain, coughing of blood, and fatigue are strongly associated with different stages of lung cancer. The model created in this study can be used to identify people at a high risk of advancing lung cancer and guide the creation of public health policies and clinical guidelines for preventing and detecting lung cancer early. Despite some limitations, such as the possibility of other unknown factors influencing the results, this research provides valuable information on the risk factors linked to different stages of lung cancer and the usefulness of ordinal logistic regression in predicting cancer stages based on these factors. The classification performance of the model has been shown by various evaluation metrics and according to these metrics, this model has performed quite well.