Comparative analysis of explainable AI models for predicting lung cancer using diverse datasets
Shahin Makubhai,
Ganesh R Pathak,
Pankaj R Chandre
Abstract:Lung cancer prediction is crucial for early detec-tion and treatment, and explainable AI models have gained attention for their interpretability. This study aims to compare various explainable AI models using diverse datasets for lung cancer prediction. Clinical, genomic, and imaging data from multiple sources were collected, prepro-cessed, and used to train models such as Logistic Regression, SVC-Linear, SVC-rbf, Decision Tree, Random Forest, AdaBoost Classifier, and XGBoost Classifier. Preliminary results in… Show more
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