Predictive Model of Functional Exercise Compliance of Patients with Breast Cancer Based on Decision Tree

Zebing Luo,1,2,* Baolin Luo,3,* Peiru Wang,4 Jinhua Wu,5 Chujun Chen,6 Zhijun Guo,1 Yiru Wang1 1Nursing Department, Cancer Hospital of Shantou University Medical College, Shantou City, People’s Republic of China; 2Nursing Department, Guangdong Provincial Key Laboratory of Breast Cancer Diagnosis and Treatment, Shantou City, People’s Republic of China; 3Department of Cardiovascular Surgery, Fujian Medical University Union Hospital, Fuzhou City, People’s Republic of China; 4Nursing Department, Shantou Central Hospital, Shantou City, People’s Republic of China; 5Nursing Department, First Affiliated Hospital of Shantou University Medical College, Shantou, People’s Republic of China; 6Mammary Department, Cancer Hospital of Shantou University Medical College, Shantou City, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yiru Wang, Nursing Department, Cancer Hospital of Shantou University Medical College, Shantou City, People’s Republic of China, Tel +8613502972031, Email stwwyyrr@163.com Chujun Chen, Mammary Department, Cancer Hospital of Shantou University Medical College, Shantou City, People’s Republic of China, Tel +86 13531260911, Email 981092602@qq.comObjective: Regular functional exercise can help recover the functions of upper limb for patients with breast cancer. By finding the influencing factors of functional exercise compliance and constructing a predictive model, patients with a poor functional exercise compliance can be better identified. This study aims to find out the factors influencing the functional exercise compliance of patients with breast cancer and build a predictive model based on decision tree.Methods: Convenience sampling was used at two tertiary hospitals in Shantou from August 2020 to March 2021. Data of patients with breast cancer patient was obtained from questionnaires and based on demographics, Constant-Murley Score, Functional Exercise Compliance Scale for Postoperative Breast Cancer Patients, Champion Health Belief Model Scale, Social Support Rating Scale, Disease Perception Questionnaire and Family Care Index Questionnaire. Possible influencing factors of functional exercise compliance were analyzed using correlation analysis as well as univariate and binary logistic regression analysis through SPSS v25, and a CHAID decision tree was used to construct a predictive model on training, validation and test sets via SPSS Modeler v18 at a ratio of 6:2:2. Prediction accuracy, sensitivity, specificity and AUC were used to analyze the efficacy of the predictive model.Results: A total of 227 valid samples were collected, of which 145 were assessed with a poor compliance (63.9%). According to a logistic regression analysis, perceived benefits, time after surgery and self-efficacy were influencing factors. The prediction accuracy, sensitivity, specificity and AUC of the predictive model, based on a CHAID decision tree analysis, were 70.73%, 57.1%, 77.8% and 0.81 respectively.Conclusion: The predictive model, based on a CHAID decision tree analysis, had a moderate predictive efficacy, which could be used as a clinical auxiliary tool for clinical nurses to predict patients’ functional exercise compliance.Keywords: breast cancer, functional exercise, predictive model, decision tree