A Medical Decision Support System Based on Support Vector Machines and the Genetic Algorithm for the Evaluation of Fetal Well-Being

OCAK H.

JOURNAL OF MEDICAL SYSTEMS, cilt.37, sa.2, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 37 Sayı: 2
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1007/s10916-012-9913-4
  • Dergi Adı: JOURNAL OF MEDICAL SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Fetal Heart Rate (FHR), Cardiotocogram (CTG), Diagnosis, Support Vector Machines (SVM), Genetic Algorithm (GA), HEART-RATE PATTERNS, CLASSIFICATION, RISK
  • Kocaeli Üniversitesi Adresli: Evet

Özet

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.