An Optimal Framework for Alzheimer’s Disease Diagnosis

作     者：Amer Alsaraira Samer Alabed Eyad Hamad Omar Saraereh 

作者机构：Biomedical Engineering DepartmentSchool of Applied Medical SciencesGerman Jordanian UniversityAmman11180Jordan Department of Electrical EngineeringFaculty of EngineeringThe Hashemite UniversityZarqa13133Jordan 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第37卷第7期

页      面：165-177页

核心收录：

学科分类：1002[医学-临床医学] 081203[工学-计算机应用技术] 08[工学] 100203[医学-老年医学] 0835[工学-软件工程] 10[医学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Biomedical engineering healthcare machine learning cognition 

摘      要：Alzheimer’s disease(AD)is a kind of progressive dementia that is frequently accompanied by brain *** the use of the morpho-logical characteristics of MRI brain scans,this paper proposed a method for diagnosing moderate cognitive impairment(MCI)and *** anatom-ical features of 818 subjects were calculated using the FreeSurfer software,and the data were taken from the ADNI *** features were first removed from the dataset after being preprocessed with an age correction algorithm using linear regression to estimate the effects of normal *** these preprocessed characteristics,the extreme learning machine served as a classifier for the diagnosis of AD and *** determining accuracy,sensitivity,specificity,and area under the curve,ten-fold cross validation was *** accuracy of AD diagnosis was 87.62 percent on average after 100 runs,while the area under curve was 94.25 *** sensitivity of the MCI diagnosis was 83.88 percent,while the accuracy was 73.38 *** age correction can help diagnose MCI more *** outcomes showed that the proposed strategy for diagnosing AD and MCI was more effective than existing methods.