Development and validation of a risk prediction model for kinesiophobia in postoperative lung cancer patients: an interpretable machine learning algorithm study

作者：Chuang Li, Youbei Lin, Xuyang Xiao, Xinru Guo, Jinrui Fei, Yanyan Lu, Junling Zhao, Lan Zhang · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-03575-7 · 被引用次数：8 · 研究领域：Cancer survivorship and care、Optimism, Hope, and Well-being、Health and Wellbeing Research

Kinesiophobia is particularly common in postoperative lung cancer patients, which causes patients may be reluctant to cough and move due to misperception, internal fear or fear of pain, and avoid rehabilitation training affecting postoperative recovery. Therefore, it is clinically important to discover the factors associated with the occurrence of kinesiophobia and to develop a prediction model. This study aims to investigate the occurrence of kinesiophobia in postoperative lung cancer patients and to develop a prediction model to assess its performance, thereby providing a reference for clinical decision-making. A cross-sectional study involving 519 postoperative lung cancer patients from a tertiary hospital in Liaoning Province was conducted. The least absolute shrinkage and selection operator (LASSO) and multifactor logistic regression were used to screen predictors. Subsequently, six machine learning (ML) models were developed and compared to identify the optimal model. The importance of feature variables was ranked and interpreted to facilitate risk assessment. The incidence of kinesiophobia among postoperative lung cancer patients was 43.74%. Positive coping style, social support, pain severity, personal income, surgical history, and gender were identified as significant predictors of kinesiophobia. Among the evaluated models, the RF model demonstrated the best performance, with an AUROC of 0.893, accuracy of 0.803, precision of 0.732, recall of 0.870, and F1 score of 0...