Classification of Lung Cancer–Related Data Using Decision Trees: A Risk Factor Interpretability Study

Ahmet Yildiz; Ferda Bozkurt

doi:10.35377/saucis...1834536

Classification of Lung Cancer–Related Data Using Decision Trees: A Risk Factor Interpretability Study

Abstract

In this study, lung cancer—related individual and environmental risk factors were analyzed using a Decision Tree–based machine learning approach. Two open-access datasets (“survey lung cancer.csv” and “lung-cancer.data”) were utilized. Since the survey dataset does not contain a direct clinical diagnosis label, the “SMOKING” variable was selected as the target class, and the study was designed as a risk-based classification task rather than a disease diagnosis model. Data preprocessing included handling missing values, encoding categorical variables, and feature normalization. The Decision Tree model was implemented in the MATLAB environment and evaluated using accuracy, precision, recall, specificity, F1-score, and AUC metrics. The model achieved an accuracy of 87.1% with a high Recall (Sensitivity) value of 0.918 and an AUC of 0.93, indicating effective identification of individuals carrying dominant lung cancer—related risk factors. These findings demonstrate that Decision Trees provide an interpretable and practical framework for risk-oriented screening and decision-support applications in healthcare.

Keywords

Ethical Statement

All datasets used in this study were obtained from publicly available sources and do not contain any personal, identifiable, or confidential patient information. Therefore, ethical committee approval was not required for this study.

Thanks

We would like to express our sincere gratitude to Prof. Dr. Mehmet Recep Bozkurt for his valuable contributions.

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Details

Primary Language

English

Subjects

Computing Applications in Health , Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Ahmet Yildiz
0009-0009-2338-3466
Türkiye

Ferda Bozkurt ^*
0000-0002-8398-3780
Türkiye

Early Pub Date

March 27, 2026

Publication Date

March 27, 2026

Submission Date

December 5, 2025

Acceptance Date

December 28, 2025

Published in Issue

Year 2026 Volume: 9 Number: 1

DOI

https://doi.org/10.35377/saucis...1834536

IZ

https://izlik.org/JA87KT83BN

Cite

RIS / Bibtex

APA

Yildiz, A., & Bozkurt, F. (2026). Classification of Lung Cancer–Related Data Using Decision Trees: A Risk Factor Interpretability Study. Sakarya University Journal of Computer and Information Sciences, 9(1), 226-232. https://doi.org/10.35377/saucis...1834536

AMA

1.Yildiz A, Bozkurt F. Classification of Lung Cancer–Related Data Using Decision Trees: A Risk Factor Interpretability Study. SAUCIS. 2026;9(1):226-232. doi:10.35377/saucis.1834536

Chicago

Yildiz, Ahmet, and Ferda Bozkurt. 2026. “Classification of Lung Cancer–Related Data Using Decision Trees: A Risk Factor Interpretability Study”. Sakarya University Journal of Computer and Information Sciences 9 (1): 226-32. https://doi.org/10.35377/saucis. 1834536.

EndNote

Yildiz A, Bozkurt F (March 1, 2026) Classification of Lung Cancer–Related Data Using Decision Trees: A Risk Factor Interpretability Study. Sakarya University Journal of Computer and Information Sciences 9 1 226–232.

IEEE

[1]A. Yildiz and F. Bozkurt, “Classification of Lung Cancer–Related Data Using Decision Trees: A Risk Factor Interpretability Study”, SAUCIS, vol. 9, no. 1, pp. 226–232, Mar. 2026, doi: 10.35377/saucis...1834536.

ISNAD

Yildiz, Ahmet - Bozkurt, Ferda. “Classification of Lung Cancer–Related Data Using Decision Trees: A Risk Factor Interpretability Study”. Sakarya University Journal of Computer and Information Sciences 9/1 (March 1, 2026): 226-232. https://doi.org/10.35377/saucis. 1834536.

JAMA

1.Yildiz A, Bozkurt F. Classification of Lung Cancer–Related Data Using Decision Trees: A Risk Factor Interpretability Study. SAUCIS. 2026;9:226–232.

MLA

Yildiz, Ahmet, and Ferda Bozkurt. “Classification of Lung Cancer–Related Data Using Decision Trees: A Risk Factor Interpretability Study”. Sakarya University Journal of Computer and Information Sciences, vol. 9, no. 1, Mar. 2026, pp. 226-32, doi:10.35377/saucis. 1834536.

Vancouver

1.Ahmet Yildiz, Ferda Bozkurt. Classification of Lung Cancer–Related Data Using Decision Trees: A Risk Factor Interpretability Study. SAUCIS. 2026 Mar. 1;9(1):226-32. doi:10.35377/saucis. 1834536