Comparative analysis of explainable artificial intelligence models for predicting lung cancer using diverse datasets

Abstrak

Lung cancer prediction is crucial for early detection and treatment, and explainable artificial intelligence (XAI) models have gained attention for their interpretability. This study aims to compare various XAI models using diverse datasets for lung cancer prediction. Clinical, genomic, and imaging data from multiple sources were collected, preprocessed, and used to train models such as logistic regression (LR), support vector classifier (SVC)-linear, SVC-radial basis function (RBF), decision tree (DT), random forest (RF), adaboost classifier, and XGBoost classifier. Preliminary results indicate that RF achieved the highest accuracy of 98.9% across multiple datasets. Evaluation metrics such as accuracy, precision, recall, and F1 score were utilized, along with interpretability techniques like feature importance rankings and rule extraction methods. The study's findings will aid in identifying effective and interpretable AI models, facilitating early detection and treatment decisions for lung cancer