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Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features.

Journal of the Formosan Medical Association = Taiwan yi zhi - 1 Jun 2024

Huang Yu-Hua, Chen Yi-Chun, Ho Wei-Min, Lee Ren-Guey, Chung Ren-Hua, Liu Yu-Li, Chang Pi-Yueh, Chang Shih-Cheng, Wang Chaung-Wei, Chung Wen-Hung, Tsai Shih-Jen, Kuo Po-Hsiu, Lee Yun-Shien, Hsiao Chun-Chieh

Abstract excerpt

BACKGROUND: Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated. METHODS: A total of 184 probable AD patients and 3773 healthy individuals aged 65 and over were enrolled. AD-related genes (51 SNPs) and 8 environmental factors were selected as features...

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Identifiers

PMID: 38044212
DOI: 10.1016/j.jfma.2023.10.021

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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