Construction and validation of risk prediction model of psychological distress in young and middle-aged patients with gynecologic malignancy based on random forest algorithm
10.3760/cma.j.cn211501-20230327-00743
- VernacularTitle:基于随机森林算法的中青年妇科恶性肿瘤患者心理痛苦预测模型构建及验证
- Author:
Shumei ZHUANG
1
;
Shimei JIN
;
Yannan CHEN
;
Xueying ZHOU
;
Yitong QU
Author Information
1. 天津医科大学护理学院，天津 300070
- Keywords:
Adolescent;
Middle aged;
Gynecologic malignancy;
Psychological distress;
Random forest;
Prediction model
- From:
Chinese Journal of Practical Nursing
2023;39(30):2366-2373
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a prediction model of psychological distress risk in young and middle-aged patients with gynecologic malignancy based on random forest algorithm and validate its prediction effect, which provided a tool for healthcare professionals to detect patients′ psychological distress in early stage.Methods:This was a cross-sectional study, a total of 385 cases of young and middle-aged patients with gynecologic malignancies admitted to the gynecology and oncology departments of six tertiary hospitals in Tianjin from October 2021 to October 2022 were consecutively included, the study subjects were randomly divided into 270 cases in the training set and 115 cases in the testing set according to 7:3 by R-studio software. After grouping the training set patients according to the presence or absence of psychological distress (positive psychological distress 151 cases and negative psychological distress 119 cases), univariate analysis was performed on each influencing factor. A random forest model for the prediction of psychological distress in young and middle-aged gynecological malignancy patients using R-studio software on the training set, and the prediction effect was verified on the testing set.Results:The prediction accuracy was 94.78%, sensitivity was 96.88%, specificity was 92.16%, positive predictive value was 93.94%, negative predictive value was 95.92%, and AUC was 0.992 (95% CI 0.982-1.000). The top 5 significant predictor variables were ranked according to the average decrease in the Gini coefficient of each influencing factor in the random forest model: General Self-Efficacy Scale score, Herth Hope Index score, Perceived Social Support Scale score, Self-Rating Depression Scale score, Self-Rating Anxiety Scale score. Conclusions:In this study, the prediction model of psychological distress in young and middle-aged patients with gynecologic malignancy constructed by random forest algorithm has high predictive efficacy, which provides reference for healthcare professionals to identify patients′ psychological distress early and formulate interventions.
