Classification Method of Alzheimer's Disease Based on Population Characteristics
In Alzheimer's disease(Alzheimer's disease,AD)classification research,data sets such as images and biomarkers contain few samples and high acquisition costs.To cope with this problem,the paper proposes a method of modeling based on popu-lation characteristics,and conducts experiments on the CMDS data set.First,the PAR method is used to analyze the correlation be-tween features and AD,and select features based on the analysis results.Then,the ADASYN algorithm is used to solve the problem of unbalanced training set samples.Finally,the XGBoost algorithm for training is used to obtain the final model.The accuracy and recall rates of the model reached 79.5%and 77.6%,and the AUC reached 0.83.The experimental results prove the effectiveness of this method.
国家科技期刊平台
NSTL
万方数据
