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Comparative Study
. 2024 May 28:12:1380034.
doi: 10.3389/fpubh.2024.1380034. eCollection 2024.

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU

Fatima Sadjadpour  1 , Niyousha Hosseinichimeh  1 , Vida Abedi  2 , Lamia M Soghier  3   4   5
Affiliations
Comparative Study

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU

Fatima Sadjadpour et al. Front Public Health. .

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.

Study design: 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.

Keywords: NICU; logistic regression; machine learning; neonatal intensive care unit; parental depression; screening system.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Correlation plot.
Figure 2
Figure 2
Process chart for model development.
Figure 3
Figure 3
Performance metrics for machine learning models.
Figure 4
Figure 4
SHAP value presenting impact on model output (for output label “1”: high risk class).
Figure 5
Figure 5
ROC curves for machine learning models.
See this image and copyright information in PMC

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References

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Publication types

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Data for this study were originally obtained from research funded through a Patient-Centered Outcomes Research Institute® (PCORI®) Award (IHS-1403-11567). The statements presented in this work are solely the responsibility of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute® (PCORI®). This project was funded under grant number R18HS029458 from the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services (HHS). The authors are solely responsible for this document’s contents, findings, and conclusions, which do not necessarily represent the views of AHRQ. Readers should not interpret any statement in this report as an official position of AHRQ or of HHS. None of the authors has any affiliation or financial involvement that conflicts with the material presented in this report. Additional support for this research was provided by the Virginia Tech Institute for Society, Culture and Environment.