基于机器学习的产妇健康与婴儿行为特征之间的关系建模。
基于机器学习的产妇健康与婴儿行为特征之间的关系建模。
IF 2.9 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
摘要
本研究通过建立一个数学模型，阐明产妇健康指标与婴儿行为特征和睡眠质量之间的关系，从而研究产妇健康对婴儿发育的影响。本研究的主要贡献如下：(1) 利用斯皮尔曼相关系数进行相关性分析，根据产妇指标探讨影响婴儿行为特征的主要因素。(2) 利用机器学习技术，包括随机森林（RF）和多层感知器（MLP），建立了一个综合模型，以确定产妇健康（生理和心理健康）与婴儿行为特征之间的关系。模型分别通过真实数据进行训练和验证。(3) 使用模糊均值（FCM）动态聚类模型对婴儿睡眠质量进行分类。利用母婴指标构建 RF 回归模型来预测婴儿睡眠质量。这项研究对于深入了解产妇健康指标与婴儿发育之间的关系具有重要意义，并为今后的干预措施提供了依据。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
Modeling the relationship between maternal health and infant behavioral characteristics based on machine learning.
This study investigates the impact of maternal health on infant development by developing a mathematical model that delineates the relationship between maternal health indicators and infant behavioral characteristics and sleep quality. The main contributions of this study are as follows: (1) The use of Spearman's correlation coefficient to conduct correlation analysis and explore the main factors that influence infant behavioral characteristics based on maternal indicators. (2) The development of a combined model using machine learning techniques, including random forest (RF) and multilayer perceptron (MLP) to establish the relationship between maternal health (physical and psychological health) and infant behavioral characteristics. The model is trained and validated by the real data respectively. (3) The use of the Fuzzy C-means (FCM) dynamic clustering model to classify infant sleep quality. An RF regression model is constructed to predict infant sleep quality using maternal indicators. This study is significant in gaining a deeper understanding of the relationship between maternal health indicators and infant development, and provides a basis for future intervention measures.
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来源期刊
期刊介绍： PLOS ONE is an international, peer-reviewed, open-access, online publication. PLOS ONE welcomes reports on primary research from any scientific discipline. It provides: * Open-access—freely accessible online, authors retain copyright * Fast publication times * Peer review by expert, practicing researchers * Post-publication tools to indicate quality and impact * Community-based dialogue on articles * Worldwide media coverage
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