Machine Learning Model for Classifying the Results of Fetal Cardiotocography Conducted in High-Risk Pregnancies

This publication has 30 references indexed in Scilit:

• The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets
  PLOS ONE, 2015
• Diagnostic Value of Non Stress Test in Latent Phase of Labor and Maternal and Fetal Outcomes
  Global Journal of Health Science, 2014
• The Apgar Score and Infant Mortality
  PLOS ONE, 2013
• Antepartum Evaluation of the Fetus and Fetal Well Being
  Clinical Obstetrics and Gynecology, 2012
• Using nonlinear features for fetal heart rate classification
  Biomedical Signal Processing and Control, 2012
• Fetal cardiotocography before and after water aerobics during pregnancy
  Reproductive Health, 2010
• Fetal distress evaluation using and analyzing the variables of antepartum computerized cardiotocography
  Archives of Gynecology and Obstetrics, 2009
• The Importance of Complexity in Model Selection
  Journal of Mathematical Psychology, 2000
• Use of the Hough transformation to detect lines and curves in pictures
  Communications of the ACM, 1972
• A Proposal for a New Method of Evaluation of the Newborn Infant.
  Anesthesia & Analgesia, 1953