Predicting Pre- and Post-Diagnostic Depression in Women with Abnormal Pap Screening Tests: A Neural Network Approach
Year: 2025
Type: article
Study design: Observational Study
Abstract: (1) Background: After receiving an abnormal Papanicolaou smear result, very often women fail to adhere to further procedures due to depression. Using a neural network approach, this research aimed to predict pre- and post-diagnostic depressive symptoms in women with abnormal Pap screening tests. (2) Methods: The study was conducted at the Clinical Center of Kragujevac, Serbia, among 172 women with a positive Pap screening result before and after diagnostic procedures (colposcopy/biopsy/endocervical curettage). Just before and 2 to 4 weeks after the diagnostic procedures, women filled out a socio-demographic questionnaire and the Hospital Anxiety and Depression Scale (HADS). Multilayer perceptron neural networks were modeled. (3) Results: Depression was present in 37.2% of women before diagnostic procedures and in 48.3% after. Feature selection showed four variables that correlated with depression before diagnostic procedures-anxiety (according to the HADS), depression according to the CESD scale, worry score on the POSM scale and use of sedatives. Model for predicting pre-diagnostic depression yielded an accuracy of 79.41%, with a value of 0.842 for area under the receiver operating characteristic curve (AUROC). The HADS anxiety score, place of residence and CESD score were the most important attributes for predicting post-diagnostic depression, with an ANN model accuracy of 88.24% and AUROC 0.939. (4) Conclusions: This research revealed a possible way of predicting depression occurrence in those women who received a positive Pap screening test and who are undergoing follow-up diagnostics, aiding medical doctors in the provision of successful and on-time psychological assistance.
Source: Life
DOI: 10.3390/life15071041
Institutions: University of Belgrade, University of Kragujevac
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4411824023
