Back propagation artificial neural network for community Alzheimer's disease screening in China

作     者：Jun Tang Lei Wu Helang Huang Jiang Feng Yefeng Yuan Yueping Zhou Peng Huang Yan Xu Chao Yu 

作者机构：Department of EpidemiologyPublic Health InstituteNanchang University Department of ChemistryPublic Health InstituteNanchang University Department of Psychosomatic MedicineFirst Affiliated Hospital of Nanchang University 

出 版 物：《Neural Regeneration Research》 (中国神经再生研究（英文版）)

年 卷 期：2013年第8卷第3期

页      面：270-276页

核心收录：

学科分类：1002[医学-临床医学] 100203[医学-老年医学] 10[医学] 

基　　金：supported by the National Natural Science Foundation of China No.30760214 

主　　题：neural regeneration clinical practice artificial neural network Alzheimer's disease mathematicalmodel community trace elements neurotransmitters grant-supported paper neuroregeneration 

摘      要：AIzheimer＇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.