Development of a Machine Learning Algorithm‐Based Predictive Model for Physical Activity Levels in Lung Cancer Survivors: A Cross‐Sectional Study

作者：Qiaoqiao Ma, Rui Wang, Mengyan Mo, Jing Luo, Yan Wang, Zerong Lian, Yaqian Du, Yongyan Xiang, Xiaoqing Liu, Huxing Cao, Lili Hou · 发表于：Journal of Clinical Nursing · 年份：2025 · DOI：10.1111/jocn.70033 · 被引用次数：3 · 研究领域：Cancer survivorship and care、Cancer-related cognitive impairment studies、Physical Activity and Health

AIMS: To investigate the physical activity levels of lung cancer survivors, analyse the influencing factors, and construct a predictive model for the physical activity levels of lung cancer survivors based on machine learning algorithms. DESIGN: This was a cross-sectional study. METHODS: Convenience sampling was used to survey lung cancer survivors across 14 hospitals in eastern, central, and western China. Data on demographic, disease-related, health-related, physical, and psychosocial factors were also collected. Descriptive analyses were performed using SPSS 25.0, and predictors were identified through multiple logistic regression analyses. Four machine learning models-random forest, gradient boosting tree, support vector machine, and logistic regression-were developed and evaluated based on the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC), accuracy, precision, recall, and F1 score. The best model was used to create an online computational tool using Python 3.11 and Flask 3.0.3. This study was conducted and reported in accordance with the TRIPOD guidelines and checklist. RESULTS: Among the 2231 participants, 670 (30%), 1185 (53.1%), and 376 (16.9%) exhibited low, moderate, and high physical activity levels, respectively. Multivariate logistic regression identified 15 independent influencing factors: residential location, geographical region, religious beliefs, histological type, treatment modality, regional lymph node stage, grip strength, 6-min ...