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

Huang, Yu-HuaYu-HuaHuangChen, Yi-ChunYi-ChunChenHo, Wei-MinWei-MinHoLee, Ren-GueyRen-GueyLeeChung, Ren-HuaRen-HuaChungLiu, Yu-LiYu-LiLiuChang, Pi-YuehPi-YuehChangChang, Shih-ChengShih-ChengChangWang, Chaung-WeiChaung-WeiWangChung, Wen-HungWen-HungChungTsai, Shih-JenShih-JenTsaiPO-HSIU KUOLee, Yun-ShienYun-ShienLeeHsiao, Chun-ChiehChun-ChiehHsiao2024-03-062024-03-062023-12-020929-6646https://scholars.lib.ntu.edu.tw/handle/123456789/640434Alzheimer'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.enAlzheimer's disease; Artificial neural networks (ANNs); Machine learning; Single nucleotide polymorphisms; Whole-genome genotyping; Whole-genome sequencing[SDGs]SDG3Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental featuresjournal article10.1016/j.jfma.2023.10.021380442122-s2.0-85178584608https://api.elsevier.com/content/abstract/scopus_id/85178584608