NDLI: Binary particle swarm optimization for feature selection in detection of infants with hypothyroidism

Content Provider	IEEE Xplore Digital Library
Author	Zabidi, A. Khuan, L.Y. Mansor, W. Yassin, I.M. Sahak, R.
Copyright Year	2011
Description	Author affiliation: Faculty of Electrical Engineering, University Teknologi Mara, 40450 Shah Alam, Selangor, Malaysia (Zabidi, A.; Khuan, L.Y.; Mansor, W.; Yassin, I.M.; Sahak, R.)
Abstract	Hypothyroidism in infants is caused by the insufficient production of hormones by the thyroid gland. Due to stress in the chest cavity as a result of the enlarged liver, their cry signals are unique and can be distinguished from the healthy infant cries. This study investigates the effect of feature selection with Binary Particle Swarm Optimization on the performance of MultiLayer Perceptron classifier in discriminating between the healthy infants and infants with hypothyroidism from their cry signals. The feature extraction process was performed on the Mel Frequency Cepstral coefficients. Performance of the MLP classifier was examined by varying the number of coefficients. It was found that the BPSO enhances the classification accuracy while reducing the computation load of the MLP classifier. The highest classification accuracy of 99.65% was achieved for the MLP classifier, with 36 filter banks, 5 hidden nodes and 11 BPS optimised MFC coefficients.
Starting Page	2772
Ending Page	2775
File Size	480661
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6090759
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pediatrics Filter banks Feature extraction Accuracy Optimization Particle swarm optimization Classification algorithms
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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