Predicting Depression, Anxiety, and Their Comorbidity among Patients with Breast Cancer in China Using Machine Learning: A Multisite Cross-Sectional Study.

ABSTRACT: Depression and anxiety are highly prevalent among patients with breast cancer. We tested the capacity of personal resources (psychological resilience, social support, and process of recovery) for predicting depression, anxiety, and comorbid depression and anxiety (CDA) among such patients using machine learning (ML). We conducted a cross-sectional survey in Liaoning Province, China, including questions about demographics, COVID-19's impact, and personal resources (707 valid responses). In the training set, we used Lasso logistic regression to establish personal resource models. Subsequently, we used six ML methods and a tenfold cross-validation strategy to establish models combining personal resources, demographics, and COVID-19 impacts. Findings indicate that in total, 21.9%, 35.1%, and 1

SUBMITTER: Li S 

PROVIDER: S-EPMC11918714 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature