Fetal health status prediction based on maternal clinical history using machine learning techniques
Fetal health status prediction based on maternal clinical history using machine learning techniques
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DOI：
10.1016/j.cmpb.2018.06.010
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发表时间：
2018-09-01
影响因子：
6.1
通讯作者：
Topcu, Varol
中科院分区：
文献类型：
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作者：
Akbulut, Akhan;Ertugrul, Egemen;Topcu, Varol
背景和目的：先天性异常的比例为 1-3%，主要通过怀孕期间的双重、三重和四重测试来找出其概率。此外，胎儿的超声评估可以增强对这些异常的检测和定义。大约60-70%的异常可以通过超声检查诊断出来，而剩下的30-40%可以在分娩后诊断出来。医学诊断和预测是与电子健康和机器学习密切相关的课题。电子医疗应用至关重要，尤其是对于无法看医生或任何医疗专业人员的患者而言。我们的目标是帮助临床医生和家庭在使用机器学习技术和电子健康应用程序的传统妊娠测试之外更好地预测胎儿先天性异常。方法：在这项工作中，我们开发了一个具有辅助电子健康应用程序的预测系统，孕妇和从业者都可以使用。对 9 个二元分类模型（平均感知器、增强决策树、贝叶斯点机、决策森林、决策丛林、局部深层支持向量机、逻辑回归、神经网络、支持向量机）进行了性能比较（考虑准确性、Fl 分数、AUC 测量），这些模型使用 96 名孕妇的临床数据集进行训练，并用于处理数据以根据母体和临床数据预测胎儿异常状态。该数据集是通过产妇问卷调查和对土耳其伊斯坦布尔 RadyoEmar 放射诊断中心的 3 名临床医生的详细评估获得的。我们的电子健康应用程序用于获取孕妇的健康状况和临床病史参数作为输入，推荐她们在怀孕期间进行的身体活动，并告知从业者和最终患者可能的胎儿异常风险作为输出。结果：在本文中，在决策森林模型的开发测试中，预测的最高准确度为 89.5%。在 16 位用户的实际测试中，性能为 87.5%。这一估计足以在患者就诊之前了解胎儿的健康状况。结论：拟议的工作旨在通过在线系统为孕妇和临床医生提供辅助服务，该在线系统由患者的移动端、临床医生的网络应用程序端和预测系统组成。此外，我们还展示了妊娠期某些临床数据参数对胎儿健康状况的影响，将这些参数与胎儿异常的存在进行统计关联，并为未来的研究提供指导。 (C) 2018 Elsevier B.V. 保留所有权利。
Background and Objective: Congenital anomalies are seen at 1-3% of the population, probabilities of which are tried to be found out primarily through double, triple and quad tests during pregnancy. Also, ultra-sonographical evaluations of fetuses enhance detecting and defining these abnormalities. About 60-70% of the anomalies can be diagnosed via ultra-sonography, while the remaining 30-40% can be diagnosed after childbirth. Medical diagnosis and prediction is a topic that is closely related with e-Health and machine learning. e-Health applications are critically important especially for the patients unable to see a doctor or any health professional. Our objective is to help clinicians and families to better predict fetal congenital anomalies besides the traditional pregnancy tests using machine learning techniques and e-Health applications.Methods: In this work, we developed a prediction system with assistive e-Health applications which both the pregnant women and practitioners can make use of. A performance comparison (considering Accuracy, Fl-Score, AUC measures) was made between 9 binary classification models (Averaged Perceptron, Boosted Decision Tree, Bayes Point Machine, Decision Forest, Decision Jungle, Locally-Deep Support Vector Machine, Logistic Regression, Neural Network, Support Vector Machine) which were trained with the clinical dataset of 96 pregnant women and used to process data to predict fetal anomaly status based on the maternal and clinical data. The dataset was obtained through maternal questionnaire and detailed evaluations of 3 clinicians from RadyoEmar radiodiagnostics center in Istanbul, Turkey. Our e-Health applications are used to get pregnant women's health status and clinical history parameters as inputs, recommend them physical activities to perform during pregnancy, and inform the practitioners and finally the patients about possible risks of fetal anomalies as the output.Results: In this paper, the highest accuracy of prediction was displayed as 89.5% during the development tests with Decision Forest model. In real life testing with 16 users, the performance was 87.5%. This estimate is sufficient to give an idea of fetal health before the patient visits the physician.Conclusions: The proposed work aims to provide assistive services to pregnant women and clinicians via an online system consisting of a mobile side for the patients, a web application side for their clinicians and a prediction system. In addition, we showed the impact of certain clinical data parameters of pregnant on the fetal health status, statistically correlated the parameters with the existence of fetal anomalies and showed guidelines for future researches. (C) 2018 Elsevier B.V. All rights reserved.