山西医科大学学报2026，Vol.57Issue(2)：215-222,8.DOI:10.13753/j.issn.1007-6611.2026.02.014
基于动态阈值调整特征选择下Transformer模型对阿尔茨海默病病程分类
Classification of Alzheimer's disease progression using Transformer model with feature selection via dynamic threshold ad-justment
摘要
Abstract
Objective To leverage a Transformer model for integrating structural magnetic resonance imaging(sMRI)data with demo-graphic information to classify Alzheimer's disease(AD)progression stages.Methods The data were sourced from the Alzheimer's Disease Neuroimaging Initiative database(ADNI).A total of 543 subjects were randomly selected,including 139 with normal cognition(NC),220 with early mild cognitive impairment(EMCI),108 with late mild cognitive impairment(LMCI),and 76 with Alzheimer's disease(AD).The dynamic threshold adjustment-based L1 regularization(L1-DTFS)and gradient boosting decision tree(GBDT-DTFS)algorithms were applied to the 272 sMRI features of these subjects to perform feature selection and identify the optimal feature subsets.The selected sMRI features,along with three demographic indicators(age,gender,and education level)and the Mini-Mental State Examination(MMSE)score,were input into the Transformer model and the logistic regression(LR)model.Their performance was assessed across all six pairwise classification tasks along the Alzheimer's disease continuum(NC vs EMCI,NC vs LMCI,NC vs AD,EMCI vs LMCI,EMCI vs AD,and LMCI vs AD),with the discriminative power quantified by the area under the receiver ope-rating characteristic curve(AUC).Results Both feature selection methods,L1-DTFS and GBDT-DTFS,successfully identified the most contributive dominant features across all six classification groups.Notably,the Transformer model incorporating L1-DTFS achieved perfect performance(100%accuracy,precision,and sensitivity)with an AUC of 1.00 in classifying the NC versus LMCI groups.Conclusion The Transformer model demonstrates robust and stable performance in classifying the stages of Alzheimer's disease progression,with particularly superior results in distinguishing between the NC and LMCI stages.关键词
阿尔茨海默病/轻度认知障碍/磁共振成像/Transformer模型/LR模型/特征选择算法/动态阈值Key words
Alzheimer's disease/mild cognitive impairment/magnetic resonance imaging/Transformer model/Logistic re-gression model/feature selection algorithm/dynamic threshold分类
医药卫生引用本文复制引用
施转芳,范炤..基于动态阈值调整特征选择下Transformer模型对阿尔茨海默病病程分类[J].山西医科大学学报,2026,57(2):215-222,8.基金项目
山西省留学回国人员科技活动择优资助项目(619017) （619017）
山西省重点研发计划国际合作项目(201803D421068) （201803D421068）
