City Research Online

Abstract

This study aimed to describe and compare background factors and symptoms at diagnosis of patients with non-advanced or advanced stage lung cancer and patients without cancer, and to develop predictive models identifying key variables that contribute to the detection of early and late-stage lung cancer. Univariate logistic regression and three machine learning algorithms were used. Compared to patients without cancer, six background factors and two symptoms differed in non-advanced lung cancer, while 11 background factors and 19 symptoms differed in advanced cases. The machine learning models showed moderate performance in classifying patients with lung cancer from those without cancer. Notably, top predictors extended beyond classic respiratory symptoms. Demographic and lifestyle factors, particularly age, smoking status, and living situation, remained essential alongside symptoms such as pain, appetite loss, weight reduction, and respiratory problems. These findings support integrating clinical, demographic, and patient-reported symptoms to improve lung cancer risk models and refine referral decisions in screening pathways.

Publication Type:	Article
Additional Information:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publisher Keywords:	Cancer, Diseases, Health care, Medical research, Oncology, Risk factors
Subjects:	R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Departments:	School of Health & Medical Sciences School of Health & Medical Sciences > Department of Nursing & Midwifery
SWORD Depositor:	Symplectic Administrator

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