Intelligent Evaluation Of Structural Ability In Patients With Alzheimer’s Disease

Alzheimer’s disease(AD)is the most common form of dementia in the elderly.Because the symptoms are not obvious in the early stage of the disease,once diagnosed,it will reach the middle and late stage of missing the optimal intervention period,which will seriously threaten the physical and mental health of the victims and their families,and bring a huge burden to their lives.Because of its multiple causes,so far there is no absolute cure.Therefore,it is necessary to carry out early screening for the elderly.In order to solve the difficulties in the assessment of structural ability when there are insufficient medical personnel for AD screening under the condition of large sample in the community,this paper starts from the most commonly used clinical screening scale and studies its structural ability test.The model was established to conduct intelligent evaluation on the replication results of structural capability.The main research contents are as follows:(1)In the second chapter,the structural ability of 109 AD patients was studied by using the random forest method.Firstly,features were extracted from the structural ability replication results of 109 AD patients,and then the model established by random forest was used for experimental verification.The results show that the accuracy of the model is up to 97%.Finally,the generalization performance of the model is analyzed.(2)In order to avoid feature extraction,the convolutional neural network method is adopted in Chapter 3 to establish an end-to-end evaluation model for the replication results of structural capability.Firstly,the structural ability test pictures of 109 AD patients were directly put into the convolutional neural network model for training test.The results showed that the accuracy of the model was 94.45%.Then,668 newly recruited samples after denoising were added to the model for training analysis,and the results showed that the accuracy of the model was 93.13%.Finally,the model was analyzed,and it was proved that the model had good performance and separability.To some extent,the two models solve the problems existing in the condition of large sample in the community,which is of great significance for the initial screening of AD patients.In addition,deep learning is the mainstream of current research and deep learning models avoid the extraction features of random forest models,so deep learning models can be further developed and put into practical use.