Fertility, Infertility and Perinatology Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Department of Health Information Technology, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Technol Health Care. 2022;30(4):951-965. doi: 10.3233/THC-213628.
Timely and accurate diagnosis of genetic diseases can lead to proper action and prevention of irreparable events.
In this work we propose an integrated genetic-neural network (GNN) to improve the prediction risk of trisomy diseases including Down's syndrome (T21), Edwards' syndrome (T18) and Patau's Syndrome (T13).
A dataset including 561 pregnant were created. In this integrated model, the structure and input parameters of the proposed multilayer feedforward network (MFN) were optimized.
The results of execution of the GNN on the testing dataset showed that the developed model can be accurately classify the anomalies from healthy fetus with 97.58% accuracy rate, and 99.44% and 85.65% sensitivity, and specificity, respectively. In the proposed GNN model, the Levenberg Merquident (LM) algorithm, the Radial Basis (Radbas) function from various types of functions were selected by the proposed GA. Moreover, maternal age, Nuchal Translucency (NT), Crown-rump length (CRL), Pregnancy-associated plasma protein A (PAPP-A) were selected by the proposed GA as the most effective factors for classifying the healthfetuses from the cases with fetal disorders.
The proposed computerized model increases the diagnostic performance of the physicians especially in the accurate detection of healthy fetus with non - invasive and low - cost treatments.
及时准确地诊断遗传病可以采取适当的行动，防止不可挽回的事件发生。
在这项工作中，我们提出了一个集成的遗传神经网络（GNN），以提高包括唐氏综合征（T21）、爱德华兹综合征（T18）和帕陶氏综合征（T13）在内的三体疾病的预测风险。
创建了一个包含 561 名孕妇的数据集。在这个集成模型中，优化了所提出的多层前馈网络（MFN）的结构和输入参数。
在测试数据集上执行 GNN 的结果表明，所开发的模型可以准确地对异常和健康胎儿进行分类，准确率为 97.58%，灵敏度分别为 99.44%和 85.65%，特异性分别为 99.44%和 85.65%。在提出的 GNN 模型中，通过提出的 GA 选择了 Levenberg Merquident（LM）算法和来自各种类型函数的径向基（Radbas）函数。此外，母亲年龄、颈项透明层（NT）、头臀长（CRL）、妊娠相关血浆蛋白 A（PAPP-A）通过提出的 GA 被选为对健康胎儿和胎儿疾病病例进行分类的最有效因素。
提出的计算机模型提高了医生的诊断性能，特别是在无创和低成本治疗的情况下，能够准确地检测健康胎儿。
