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An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification
Author(s) -
Neffati Syrine,
Ben Abdellafou Khaoula,
Jaffel Ines,
Taouali Okba,
Bouzrara Kais
Publication year - 2019
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22304
Subject(s) - support vector machine , artificial intelligence , computer science , classifier (uml) , principal component analysis , pattern recognition (psychology) , machine learning , dimensionality reduction , multiclass classification , curse of dimensionality , kernel principal component analysis , cognitive impairment , kernel (algebra) , kernel method , disease , mathematics , medicine , pathology , combinatorics
Abstract Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.