Classification of malignant/benign groups in lung cancer by machine learning and investigation of feature significance of parameters

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dc.authorid0000-0003-4815-3674
dc.contributor.authorÇalışkan, Gönül
dc.contributor.authorAlataş, Emre
dc.contributor.authorKöksal, Sevilay
dc.contributor.authorAkyel, Reşit
dc.contributor.authorÇavdar, İffet
dc.contributor.authorAlçin, Göksel
dc.contributor.authorArslan, Esra
dc.contributor.authorÇiftçi-Küsbeci, Tuba
dc.contributor.authorDereli, Elçin
dc.contributor.authorAkgün, Elif
dc.contributor.authorTanyıldızı-Kökkülünk, Handan
dc.contributor.authorYiğit, Şafak
dc.date.accessioned2026-08-13T07:56:00Z
dc.date.available2026-08-13T07:56:00Z
dc.date.issued2026
dc.departmentMeslek Yüksekokulları, Meslek Yüksekokulu, Fizyoterapi Programı
dc.description.abstractThis study aimed to apply machine learning (ML) models to enhance lung cancer (LC) classification, distinguishing malignant from benign tumors, using data from 73 patients. 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. 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. 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. © 2026 the author(s), published by De Gruyter, Berlin/Boston.
dc.identifier.citationCaliskan, G., Alatas, E., Koksal, S., Yigit, S., Akyel, R., Cavdar, I., Alcin, G., Arslan, E., Ciftci-Kusbeci, T., Dereli, E., [11. yazar], & [12. yazar]. (2026). [Makale başlığı]. Biomedizinische Technik.
dc.identifier.doi10.1515/bmt-2024-0628
dc.identifier.issn00135585
dc.identifier.scopus2-s2.0-105045951568
dc.identifier.urihttps://hdl.handle.net/20.500.12941/440
dc.indekslendigikaynakScopus
dc.institutionauthorYiğit, Şafak
dc.institutionauthorid0000-0003-4815-3674
dc.language.isoen
dc.publisherWalter de Gruyter GmbH
dc.relation.ispartofBiomedizinische Technik
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectLung Cancer
dc.subjectMalignant
dc.subjectBenign
dc.subjectMachine Learning
dc.subjectXGBoost
dc.subjectSmote
dc.titleClassification of malignant/benign groups in lung cancer by machine learning and investigation of feature significance of parameters
dc.typeArticle

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