Predicting Risk of Antenatal Depression and Anxiety Using Multi-Layer Perceptrons and Support Vector Machines.
Predicting Risk of Antenatal Depression and Anxiety Using Multi-Layer Perceptrons and Support Vector Machines.
复制标题
使用多层感知器和支持向量机预测产后抑郁和焦虑的风险。
DOI：
10.3390/jpm11030199
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发表时间：
2021-03-12
影响因子：
--
通讯作者：
Waqas A
中科院分区：
文献类型：
--
作者：
Javed F;Gilani SO;Latif S;Waris A;Jamil M;Waqas A
围产期抑郁和焦虑被定义为妇女在怀孕期间、分娩前后和分娩后面临的心理健康问题。虽然这种情况经常发生在妇女身上，并影响到包括婴儿在内的所有家庭成员，但它很容易被发现和低估诊断。全世界，特别是在低收入国家，产前抑郁和焦虑的流行率极高。绝大多数人患有轻度到中度的抑郁症，有可能导致母子关系受损和婴儿健康，很少有妇女最终结束自己的生命。由于费用高和资源缺乏，几乎不可能诊断每一名孕妇患有抑郁症/焦虑症，而检测不足可能对母亲和儿童的健康产生持久影响。本工作提出了一种基于多层感知器的神经网络(MLP-NN)分类器来预测孕妇抑郁和焦虑的风险。我们在巴基斯坦500名产前妇女的数据集上对我们提出的系统进行了培训和评估。在分类器训练之前，使用ReliefF进行特征选择。使用准确度、敏感度、特异度、精确度、F1评分和受试者操作特征曲线下面积等评估指标来评估训练后模型的性能。多层感知器和支持向量分类器分别获得了88%和80%的产前抑郁和85%和77%的产前焦虑的接收工作特征曲线下面积。该系统可用作妇女在医院妇产科常规就诊期间进行筛查的促进器。
Perinatal depression and anxiety are defined to be the mental health problems a woman faces during pregnancy, around childbirth, and after child delivery. While this often occurs in women and affects all family members including the infant, it can easily go undetected and underdiagnosed. The prevalence rates of antenatal depression and anxiety worldwide, especially in low-income countries, are extremely high. The wide majority suffers from mild to moderate depression with the risk of leading to impaired child–mother relationship and infant health, few women end up taking their own lives. Owing to high costs and non-availability of resources, it is almost impossible to diagnose every pregnant woman for depression/anxiety whereas under-detection can have a lasting impact on mother and child’s health. This work proposes a multi-layer perceptron based neural network (MLP-NN) classifier to predict the risk of depression and anxiety in pregnant women. We trained and evaluated our proposed system on a Pakistani dataset of 500 women in their antenatal period. ReliefF was used for feature selection before classifier training. Evaluation metrics such as accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver operating characteristic curve were used to evaluate the performance of the trained model. Multilayer perceptron and support vector classifier achieved an area under the receiving operating characteristic curve of 88% and 80% for antenatal depression and 85% and 77% for antenatal anxiety, respectively. The system can be used as a facilitator for screening women during their routine visits in the hospital’s gynecology and obstetrics departments.
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影响因子：
6.9
作者：
Iniesta R;Stahl D;McGuffin P
通讯作者：
McGuffin P
影响因子：
4.7
作者：
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通讯作者：
House, Allan
DOI：
10.1007/978-1-60327-241-4_13
发表时间：
2010-01-01
期刊：
DATA MINING TECHNIQUES FOR THE LIFE SCIENCES
影响因子：
--
作者：
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通讯作者：
Weston, Jason
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8.5
作者：
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通讯作者：
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影响因子：
4.4
作者：
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通讯作者：
Aijaz, Anita