Application of artificial neural network model in diagnosis of Alzheimer's disease
Application of artificial neural network model in diagnosis of Alzheimer's disease
复制标题
人工神经网络模型在阿尔茨海默病诊断中的应用
DOI：
10.1186/s12883-019-1377-4
复制
发表时间：
2019-07-08
期刊：
影响因子：
2.6
通讯作者：
Zhou, Yueping
中科院分区：
文献类型：
--
作者：
Wang, Naibo;Chen, Jinghua;Zhou, Yueping
背景阿尔茨海默氏病由于发病率不断上升，已成为全球公共卫生危机。本研究的目的是利用人工神经网络（ANN）建立AD早期诊断的预警模型，并探索AD早期敏感标志物。方法采用基于人群的巢式病例对照研究设计。从2013年至2016年2482名社区居住的60岁及以上老年人队列中，选取了89例依从性良好、愿意提供尿液和血液样本的新AD病例。对于每个病例，确定了两名居住在附近的对照者。纳入尿液和血液中AD的生物标志物、神经心理功能和流行病学参数，以分析AD的潜在危险因素。与逻辑回归、k近邻（kNN）和支持向量机（SVM）模型相比，采用三层拓扑结构的反向传播神经网络来开发预警模型。所有模型的性能均通过敏感性、特异性、准确性、阳性预后值（PPV）、阴性预后值（NPV）、曲线下面积（AUC）进行测量，并使用自举重采样进行验证。结果AD组的平均年龄比非AD对照组大约5岁（P
BackgroundAlzheimer's disease has become a public health crisis globally due to its increasing incidence. The purpose of this study was to establish an early warning model using artificial neural network (ANN) for early diagnosis of AD and to explore early sensitive markers for AD.MethodsA population based nested case-control study design was used. 89 new AD cases with good compliance who were willing to provide urine and blood specimen were selected from the cohort of 2482 community-dwelling elderly aged 60years and over from 2013 to 2016. For each case, two controls living nearby were identified. Biomarkers for AD in urine and blood, neuropsychological functions and epidemiological parameters were included to analyze potential risk factors of AD. Compared with logistic regression, k-Nearest Neighbor (kNN) and support vector machine (SVM) model, back-propagation neural network of three-layer topology structures was applied to develop the early warning model. The performance of all models were measured by sensitivity, specificity, accuracy, positive prognostic value (PPV), negative prognostic value (NPV), the area under curve (AUC), and were validated using bootstrap resampling.ResultsThe average age of AD group was about 5years older than the non-AD controls (P