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

Ravindran S, Jambek AB, Muthusamy H, Neoh SC - Comput Math Methods Med (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Microelectronic Engineering, Universiti Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600 Perlis, Malaysia.

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.

Show MeSH
Implementation of IAGA in pattern classification.
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Related In: Results  -  Collection


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fig3: Implementation of IAGA in pattern classification.

Mentions: Finally, the classifier tends to classify the inputs into their respective binary or multiclass groups based on the number of the classes of the concerned dataset. For instance, when CTG dataset is subjected to the aforementioned processes, classifiers like k-NN, BN, and ELM will classify the data samples into pathological, suspect, and normal samples. Similarly, when PD dataset is subjected to the aforementioned processes, the k-NN and SVM classifiers will classify the data samples into pathological and normal samples. Figure 3 describes the implementation of the proposed IAGA methods along with the benchmark datasets.


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

Ravindran S, Jambek AB, Muthusamy H, Neoh SC - Comput Math Methods Med (2015)

Implementation of IAGA in pattern classification.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4352501&req=5

fig3: Implementation of IAGA in pattern classification.
Mentions: Finally, the classifier tends to classify the inputs into their respective binary or multiclass groups based on the number of the classes of the concerned dataset. For instance, when CTG dataset is subjected to the aforementioned processes, classifiers like k-NN, BN, and ELM will classify the data samples into pathological, suspect, and normal samples. Similarly, when PD dataset is subjected to the aforementioned processes, the k-NN and SVM classifiers will classify the data samples into pathological and normal samples. Figure 3 describes the implementation of the proposed IAGA methods along with the benchmark datasets.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Microelectronic Engineering, Universiti Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600 Perlis, Malaysia.

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.

Show MeSH