A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being

Computational and Mathematical Methods in Medicine
Sindhu RavindranSiew-Chin Neoh

Abstract

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.

References

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Nov 30, 2013·Computational and Mathematical Methods in Medicine·Ersen Yılmaz, Cağlar Kılıkçıer
Jan 1, 2013·Health Information Science and Systems·Juanying XieXiaohui Liu

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Citations

Jun 27, 2019·Communications Biology·Aubin ThomasWilliam Ritchie
Jan 12, 2020·Communications Biology·Aubin ThomasWilliam Ritchie

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Methods Mentioned

BETA
feature extraction
PCA

Software Mentioned

ANBLIR
IAGA GA
Cobweb
IAGA

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