ArticleNeural regeneration research2013
Back propagation artificial neural network for community Alzheimer's disease screening in China.
Article in Neural regeneration research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning.Frontiers in public health · 2021Article
- Deep learning-based classification of multi-categorical Alzheimer's disease data.Current neurobiology · 2019Article
- Application of artificial neural network model in diagnosis of Alzheimer's disease.BMC neurology · 2019Article
- Compound of icariin, astragalus, and puerarin mitigates iron overload in the cerebral cortex of Alzheimer's disease mice.Neural regeneration research · 2018Article
- Article
- Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model.Lipids in health and disease · 2017Article
- Neuroprotective effect of the active components of three Chinese herbs on brain iron load in a mouse model of Alzheimer's disease.Experimental and therapeutic medicine · 2015Article
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9 authors.
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Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.