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Classification of Healthy and ADHD Subjects Using Neural Network Classifier


Affiliations
1 Department of ECE, Anna University, Chennai, Tamilnadu, 600025, India
2 Department of ECE, CEG, Anna University, Chennai, Tamilnadu, 600025, India
     

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Two class classification and four class classification of healthy and Attention Deficit Hyperactivity Disorder (ADHD) subjects based on the resting state fMRI (Rs-fMRI) data of healthy and ADHD subjects using SVM and Neural network classifier have been done in this paper. Under two class classification, classification of healthy and ADHD classes for all the subjects and classification based on gender for healthy and ADHD subjects was performed. Classification in SVM for both cases yielded results comparative to that of neural network classifier. In the case of healthy and ADHD classification, SVM showed 64% classification whereas in neural network classifier the two classes were completely classified. Also, gender based classification for both classes in SVM yielded poor results compared to neural network classifier. Under Four class classification, classification among Normal-Male, Normal-Female, Abnormal-Male, Abnormal-Female was done using Neural network Classifier.


Keywords

FMRI, ADHD, SVM Classification, Laterality Cofactors, Neural Network Classifier.
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  • Classification of Healthy and ADHD Subjects Using Neural Network Classifier

Abstract Views: 260  |  PDF Views: 3

Authors

P. Udaya Prasanth
Department of ECE, Anna University, Chennai, Tamilnadu, 600025, India
V. Solai Revathi
Department of ECE, CEG, Anna University, Chennai, Tamilnadu, 600025, India
O. Uma Maheswari
Department of ECE, CEG, Anna University, Chennai, Tamilnadu, 600025, India

Abstract


Two class classification and four class classification of healthy and Attention Deficit Hyperactivity Disorder (ADHD) subjects based on the resting state fMRI (Rs-fMRI) data of healthy and ADHD subjects using SVM and Neural network classifier have been done in this paper. Under two class classification, classification of healthy and ADHD classes for all the subjects and classification based on gender for healthy and ADHD subjects was performed. Classification in SVM for both cases yielded results comparative to that of neural network classifier. In the case of healthy and ADHD classification, SVM showed 64% classification whereas in neural network classifier the two classes were completely classified. Also, gender based classification for both classes in SVM yielded poor results compared to neural network classifier. Under Four class classification, classification among Normal-Male, Normal-Female, Abnormal-Male, Abnormal-Female was done using Neural network Classifier.


Keywords


FMRI, ADHD, SVM Classification, Laterality Cofactors, Neural Network Classifier.