Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States.
Department of Public Health Sciences, Penn State University, College of Medicine, Hershey, PA, United States.
Front Public Health. 2024 May 28;12:1380034. doi: 10.3389/fpubh.2024.1380034. eCollection 2024.
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.
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 ( = 300) from 2016 to 2017. This dataset includes a comprehensive list of demographic characteristics, depression and stress symptoms, social support, and parent/infant factors.
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.
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.
Logistic regression is effective in identifying parents at risk of depression for targeted screening with a performance comparable to common ML-based models.
新生儿重症监护病房（NICU）入院对父母来说是一种压力很大的经历。与一般分娩人群相比，NICU 父母患抑郁症状的风险高两倍。父母的心理健康问题对父母和婴儿都有长期的不良影响。及时的筛查和治疗可以减少这些负面影响。
我们的目的是比较传统逻辑回归与其他机器学习（ML）模型在识别更有可能出现抑郁症状的父母方面的性能，以便优先对高危父母进行筛查。我们使用了 2016 年至 2017 年从儿童国家医院（ = 300）出院的 NICU 婴儿的父母获得的数据。该数据集包括人口统计学特征、抑郁和压力症状、社会支持以及父母/婴儿因素的综合列表。
我们的研究设计优化了八种 ML 算法-逻辑回归、支持向量机、决策树、随机森林、XGBoost、朴素贝叶斯、K-最近邻和人工神经网络-以确定与父母抑郁相关的主要风险因素。我们根据接受者操作特征曲线（AUC）、阳性预测值（PPV）、灵敏度和 F 分数来比较模型。
结果表明，所有八种模型的 AUC 均高于 0.8，这表明基于逻辑回归的模型的性能与其他常见的 ML 模型相当。
逻辑回归在识别有抑郁风险的父母进行针对性筛查方面是有效的，其性能可与常见的基于 ML 的模型相媲美。
