Wang, C.W. (2006) New Ensemble Machine Learning Method for Classification and Prediction on Gene Expression Data. Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE, 2006, 3478-3481.

has been cited by the following article:

• TITLE: Analysis of Cardiotocogram Data for Fetal Distress Determination by Decision Tree Based Adaptive Boosting Approach

  AUTHORS: Esra Mahsereci Karabulut, Turgay Ibrikci

  KEYWORDS: Cardiotocogram, Fetal Distress, Adaptive Boosting, Decision Tree

  JOURNAL NAME: Journal of Computer and Communications, Vol.2 No.9, July 11, 2014

  ABSTRACT: Cardiotocography is one of the most widely used technique for recording changes in fetal heart rate (FHR) and uterine contractions. Assessing cardiotocography is crucial in that it leads to iden- tifying fetuses which suffer from lack of oxygen, i.e. hypoxia. This situation is defined as fetal dis- tress and requires fetal intervention in order to prevent fetus death or other neurological disease caused by hypoxia. In this study a computer-based approach for analyzing cardiotocogram in- cluding diagnostic features for discriminating a pathologic fetus. In order to achieve this aim adaptive boosting ensemble of decision trees and various other machine learning algorithms are employed.