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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 283532, 11 pages
Research Article

A Novel Clinical Decision Support System Using Improved Adaptive Genetic Algorithm for the Assessment of Fetal Well-Being

1School of Microelectronic Engineering, Universiti Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600 Perlis, Malaysia
2School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600 Perlis, Malaysia
3Department of Computing Science and Digital Technologies, University of Northumbria, Newcastle NE, UK

Received 12 September 2014; Revised 16 November 2014; Accepted 16 November 2014

Academic Editor: Maria N. D. S. Cordeiro

Copyright © 2015 Sindhu Ravindran et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm.