Department of Epidemiology, Public Health Institute, Nanchang University, Nanchang 330006, Jiangxi Province, China.
Department of Chemistry, Public Health Institute, Nanchang University, Nanchang 330006, Jiangxi Province, China.
Neural Regen Res. 2013 Jan 25;8(3):270-6. doi: 10.3969/j.issn.1673-5374.2013.03.010.
Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868-0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community.
从社区中通过现场抽样选择了符合中国精神障碍分类与诊断标准第三版（CCMD-3）诊断标准的阿尔茨海默病患者。采用原子吸收法检测血样中的宏量和微量元素水平，采用放射免疫法检测神经递质。使用 SPSS 13.0 建立数据库，并使用 Clementine 12.0 软件模拟用于预测阿尔茨海默病的反向传播人工神经网络。以日常生活活动评分、肌酐、5-羟色胺、年龄、多巴胺和铝的分数作为输入变量，结果显示，反向传播人工神经网络的曲线下面积为 0.929（95%置信区间：0.868-0.968），敏感度为 90.00%，特异度为 95.00%，准确度为 92.50%。结果表明，基于上述 6 个变量建立的反向传播人工神经网络的结果，对于从社区中选择的阿尔茨海默病患者的筛查和诊断令人满意。
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