Multiclass recognition of Alzheimer’s and Parkinson’s disease using various machine learning techniques: A study  

作　　者：Chetan Balaji D.S.Suresh 

机构地区：[1]Department of ECE Channabasaveshwara Institute of Technology Tumkur,Karnataka,India Visvesvaraya Technological University Belagavi,Karnataka,India

出　　处：《International Journal of Modeling, Simulation, and Scientific Computing》2022年第1期235-250,共16页建模、仿真和科学计算国际期刊（英文）

摘　　要：The aging population is primarily affected by Alzheimer’s disease(AD)that is an incur-able neurodegenerative disorder.There is a need for an automated efficient technique to diagnose Alzheimer’s in its early stage.Various techniques are used to diagnose AD.EEG and neuroimaging methodologies are widely used to highlight changes in the electrical activity of the brain signals that are helpful for early diagnosis.Parkinson’s disease(PD)is a major neurological disease that results in an average of 50,000 new clinical diagnoses worldwide every year.The voice features are majorly used as the main means to diag-nose PD.The major symptoms of PD are loss of intensity,the monotony of loudness and pitch,reduction in stress,unidentified silences,and dysphonia.Even though various innovative models are proposed by explorers about Alzheimer’s and Parkinson’s classifi-cation diseases,still there is a need for efficient learning methodologies and techniques.This paper provides a review on using machine learning(ML)together with several fea-ture extraction techniques that is helpful in the early detection of AD with Parkinson’s.The novelty and objective of this study are that the CAD technique is used to improve the accuracy of early diagnosis of AD.The proposed technique depends on the nonlinear process for data dimension reduction,feature removal,and classification using kernel-based support vector machine(SVM)classifiers.The dimension of the input space is radically diminished with kernel methods.As the learning set is labeled,it creates sense to utilize this information to make a dependable method of dropping the input space dimension.The different techniques of ML are explained under the major approaches viz.SVM,artificial neural network(ANN),deep learning(DL),and ensemble methods.A comprehensive assessment is presented at SVM,ANN,and DL approaches for better detection of Alzheimer’s with PD highlighting future insights.

关 键 词：ELECTROENCEPHALOGRAPH support vector machine Alzheimer’s disease control normal machine learning 

分 类 号：R74[医药卫生—神经病学与精神病学]