Document Detail

A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being.
MedLine Citation:
PMID:  23321973     Owner:  NLM     Status:  In-Data-Review    
A new scheme was presented in this study for the evaluation of fetal well-being from the cardiotocogram (CTG) recordings using support vector machines (SVM) and the genetic algorithm (GA). CTG recordings consist of fetal heart rate (FHR) and the uterine contraction (UC) signals and are widely used by obstetricians for assessing fetal well-being. Features extracted from normal and pathological FHR and UC signals were used to construct an SVM based classifier. The GA was then used to find the optimal feature subset that maximizes the classification performance of the SVM based normal and pathological CTG classifier. An extensive clinical CTG data, classified by three expert obstetricians, was used to test the performance of the new scheme. It was demonstrated that the new scheme was able to predict the fetal state as normal or pathological with 99.3 % and 100 % accuracy, respectively. The results reveal that, the GA can be used to determine the critical features to be used in evaluating fetal well-being and consequently increase the classification performance. When compared to widely used ANN and ANFIS based methods, the proposed scheme performed considerably better.
Hasan Ocak
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Publication Detail:
Type:  Journal Article     Date:  2013-01-16
Journal Detail:
Title:  Journal of medical systems     Volume:  37     ISSN:  0148-5598     ISO Abbreviation:  J Med Syst     Publication Date:  2013 Apr 
Date Detail:
Created Date:  2013-01-16     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7806056     Medline TA:  J Med Syst     Country:  United States    
Other Details:
Languages:  eng     Pagination:  9913     Citation Subset:  IM    
Kocaeli University, Department of Mechatronics Engineering, Umuttepe Campus, 41380, Kocaeli, Turkey,
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