Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU.
Front Public Health
; 12: 1380034, 2024.
Article
em En
| MEDLINE
| ID: mdl-38864019
ABSTRACT
Introduction:
Neonatal intensive care unit (NICU) admission is a stressful experience for parents. NICU parents are twice at risk of depression symptoms compared to the general birthing population. Parental mental health problems have harmful long-term effects on both parents and infants. Timely screening and treatment can reduce these negative consequences.Objective:
Our objective is to compare the performance of the traditional logistic regression with other machine learning (ML) models in identifying parents who are more likely to have depression symptoms to prioritize screening of at-risk parents. We used data obtained from parents of infants discharged from the NICU at Children's National Hospital (n = 300) from 2016 to 2017. This dataset includes a comprehensive list of demographic characteristics, depression and stress symptoms, social support, and parent/infant factors. Studydesign:
Our study design optimized eight ML algorithms - Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, XGBoost, Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network - to identify the main risk factors associated with parental depression. We compared models based on the area under the receiver operating characteristic curve (AUC), positive predicted value (PPV), sensitivity, and F-score.Results:
The results showed that all eight models achieved an AUC above 0.8, suggesting that the logistic regression-based model's performance is comparable to other common ML models.Conclusion:
Logistic regression is effective in identifying parents at risk of depression for targeted screening with a performance comparable to common ML-based models.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Pais
/
Unidades de Terapia Intensiva Neonatal
/
Depressão
/
Aprendizado de Máquina
Limite:
Adult
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Female
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Humans
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Male
/
Newborn
Idioma:
En
Revista:
Front Public Health
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos