Multiclass recognition of AD neurological diseases using a bag of deep reduced features coupled with gradient descent optimized twin support vector machine classifier for early diagnosis

• Published in: Concurrency and computation 2022-09, Vol.34 (21), p.n/a
• Main Authors: Velliangiri, S., Pandiaraj, Shanthini, S, Iwin Thanakumar Joseph, Muthubalaji, S.
• Format: Article
• Language: English
• Subjects: Alzheimer's disease; Artificial neural networks; bag of deep features (BoDrF) reduction; Brain; Classifiers; Correlation coefficients; Discriminant analysis; Electroencephalography; Feature extraction; gradient decent optimizer; Model accuracy; multi class classification; Neurological diseases; Reduction; Signal classification; Signal processing; Support vector machines; twin support vector machine classifier
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.7099

Summary Alzheimer's disease (AD) is an advanced neurodegenerative disease of the brain that affects the nerve system of brain. Previously, several feature extraction and classification methods were discussed, but that methods provide high over fitting problem, which leads to minimization of detection accuracy. To overcome these issues, the multi class classification of AD diseases using bag of deep feature reduction technique and twin support vector machine classifier (TSVM) optimized with gradient decent optimizer is proposed in this manuscript for classifying the AD disease as severe AD, mild cognitive impairment, healthy control. At first, the input EEG signals are pre‐processed. To decrease the execution time and processing time with feature size, a bag of deep features reduction technique is used. The reduced feature signals are classified by optimized TSVM. The simulation process is implemented in MATLAB environment. The proposed model achieves higher accuracy 33.84%, 28.93%, 33.03%, 27.93%, higher precision 22.87%, 16.97%, 16.97%, and 36.97%, compared with the existing methods, such as piecewise aggregate approximation support vector machine (MCC‐EEG‐PAA‐SVM), convolutional neural network (MCC‐EEG‐CNN), conformal kernel‐based fuzzy support vector machine (MCC‐EEG‐CKF‐SVM), Pearson correlation coefficient‐based feature selection strategy with linear discriminant analysis classifier (MCC‐EEG‐ PCC‐LDA).