Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease

作     者：Binny Naik Ashir Mehta Manan Shah 

作者机构：Department of Computer EngineeringIndus UniversityAhmedabadGujarat 382115India Department of Chemical EngineeringSchool of TechnologyPandit Deendayal Petroleum UniversityGandhinagarGujarat 382007India 

出 版 物：《Visual Computing for Industry,Biomedicine,and Art》 (工医艺的可视计算（英文）)

年 卷 期：2020年第3卷第1期

页      面：289-306页

核心收录：

学科分类：1002[医学-临床医学] 10[医学] 

基　　金：Department of Chemical Engineering School of Technology Department of Computer Engineering Indus University Pandit Deendayal Petroleum University 

主　　题：Machine learning Support vector machine Alzheimer 

摘      要：Alzheimer’s disease(AD)is the most common type of *** exact cause and treatment of the disease are still *** neuroimaging modalities,such as magnetic resonance imaging(MRI),positron emission tomography,and single-photon emission computed tomography,have played a significant role in the study of ***,the effective diagnosis of AD,as well as mild cognitive impairment(MCI),has recently drawn large *** technological advancements,such as robots,global positioning system technology,sensors,and machine learning(ML)algorithms,have helped improve the diagnostic process of *** study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy *** have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.