Biomedizinische Technik. Biomedical engineering · 2026
Classification of malignant/benign groups in lung cancer by machine learning and investigation of feature significance of parameters.
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Caliskan Gonul, Alatas Emre, Koksal Sevilay, Yigit Safak, Akyel Resit, Cavdar Iffet, Alcin Goksel, Arslan Esra, Ciftci-Kusbeci Tuba, Dereli Elcin, Akgun Elife, Tanyildizi-Kokkulunk Handan
Аннотация
OBJECTIVES: This study aimed to apply machine learning (ML) models to enhance lung cancer (LC) classification, distinguishing malignant from benign tumors, using data from 73 patients.
METHODS: The dataset included PET/CT biomarkers and demographic factors, with SMOTE applied to address class imbalance. Three models were evaluated using 10-fold cross-validation to compare Random Forest (RF), Decision Tree (DT), Extra Trees Classifier (ETC), and XGBoost algorithms based on accuracy and AUC.
RESULTS: ETC performed best in Model 1 (86 % accuracy, AUC 0.95), RF in Model 2 (94 % accuracy, AUC 0.97), and XGBoost in Model 3 (94 % accuracy, AUC 0.98). XGBoost consistently outperformed others, particularly in Model 3, which included age and smoking. Feature importance analysis highlighted SUVmax as the most predictive variable, with smoking having a moderate influence and gender being minimal.
CONCLUSIONS: Integrating clinical and lifestyle data with PET/CT parameters significantly improved LC classification. XGBoost emerged as the most effective model, demonstrating that comprehensive models enhance diagnostic accuracy beyond traditional metrics.
METHODS: The dataset included PET/CT biomarkers and demographic factors, with SMOTE applied to address class imbalance. Three models were evaluated using 10-fold cross-validation to compare Random Forest (RF), Decision Tree (DT), Extra Trees Classifier (ETC), and XGBoost algorithms based on accuracy and AUC.
RESULTS: ETC performed best in Model 1 (86 % accuracy, AUC 0.95), RF in Model 2 (94 % accuracy, AUC 0.97), and XGBoost in Model 3 (94 % accuracy, AUC 0.98). XGBoost consistently outperformed others, particularly in Model 3, which included age and smoking. Feature importance analysis highlighted SUVmax as the most predictive variable, with smoking having a moderate influence and gender being minimal.
CONCLUSIONS: Integrating clinical and lifestyle data with PET/CT parameters significantly improved LC classification. XGBoost emerged as the most effective model, demonstrating that comprehensive models enhance diagnostic accuracy beyond traditional metrics.