Work

Constructing a Predictive Model for Psychological Distress of Young‐ and Middle‐Aged Gynaecological Cancer Patients

Year: 2024

Type: article

Abstract: BACKGROUND: Cancer patients experience substantial psychological distress which causes the reduction of the quality of life. However, the risk of psychological distress has not been well predicted especially in young- and middle-aged gynaecological cancer patients. This study aimed to develop a prediction model for psychological distress in young- and middle-aged gynaecological cancer patients using the artificial neural network (ANN). METHODS: A cross-sectional study of young- and middle-aged gynaecological cancer patients (n = 368) was conducted between March and December 2022. We used the univariate analysis to determine the factors affecting psychological distress. ANN was used for psychological distress prediction in young- and middle-aged gynaecological cancer patients. Also, a traditional logistic regression (LR) model was constructed for comparison. The area under the receiver's operating characteristic curve (AUC) was used to evaluate the model's predictive performance. RESULTS: ANN and LR showed that self-efficacy, economic income and sleep duration were the top risk variables for psychological distress in young- and middle-aged gynaecological cancer patients. The AUC of the ANN was 0.977, the sensitivity was 94.83% and the specificity was 86.44%, whereas logistic regression's were 0.920, 85.57% and 82.76%, respectively. CONCLUSION: Compared with the LR model, the ANN model shows obvious superiority across all assessment index outcomes, and it may be used as a decision-support tool for early identification of young- and middle-aged gynaecological cancer patients suffering from psychological distress.

Source: Journal of Evaluation in Clinical Practice

DOI: 10.1111/jep.14244