Optimizing Lung Cancer Prediction through Feature Selection-based Machine Learning Models

Lung cancer is one of the most prevalent diseases affecting people and is a main factor in the rising mortality rate. Recently, Machine Learning (ML) methods have been utilized to detect and classify several types of cancer, including lung cancer. These methods can assist in enhancing the accuracy of lung cancer classification. In this research, several ML models, including Decision Tree (DT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), are implemented. In addition, Recursive Feature Elimination (RFE) feature selection technique is used to select the significant features and enhance the efficiency of detecting lung cancer using two text-based datasets. Finally, a detailed comparison will be implemented to identify the most effective model for detecting lung cancer and then specify its severity. Several parameters, including accuracy, precision, recall, and F1-score, are presented to compare the performance of each classifier. The experimental results showed that applying the feature selection approach to the data enhanced the classification accuracy across both datasets.