In:
Recent Advances in Computer Science and Communications, Bentham Science Publishers Ltd., Vol. 15, No. 4 ( 2022-05)
Abstract:
Dementia is a progressive neurodegenerative brain disease emerging as a global
health problem in adults aged 65 years or above, resulting in the death of nerve cells. The elimination of redundant and irrelevant features from the datasets is however necessary for accurate detection
thus timely treatment of dementia. Methods: For this purpose, an ensemble approach of univariate and multivariate feature selection
methods has been proposed in this study. A comparison of four univariate feature selection techniques (t-Test, Wilcoxon, Entropy and ROC) and six multivariate feature selection approaches (ReliefF,
Bhattacharyya, CFSSubsetEval, ClassifierAttributeEval, CorrelationAttributeEval, OneRAttributeEval) has been performed. The ensemble of best univariate & multivariate filter algorithms is
proposed which helps in acquiring a subset of features that includes only relevant and non-redundant features. The classification is performed using Naïve Bayes, k-NN, and Random Forest algorithms. Results: Experimental results show that t-Test and ReliefF feature selection is capable of selecting
10 relevant features that give the same accuracy when all features are considered. In addition to it, the accuracy obtained using k-NN with an ensemble approach is 99.96%. The statistical significance
of the method has been established using Friedman’s statistical test. Conclusion: The new ranking criteria computed by the ensemble method efficiently eliminate the
insignificant features and reduces the computational cost of the algorithm. The ensemble method has been compared to the other approaches for ensuring the superiority of the proposed model. Discussion: The percentage gain in accuracy for all three classifiers, Naïve Bayes, k-NN, and Random
Forest shows a remarkable difference noted down for the percentage gain in the accuracies after applying feature selection using Naïve Bayes and k-NN. Using univariate filter selection methods,
the t-test is outshining among all the methods while selecting only 10 feature subsets.
Type of Medium:
Online Resource
ISSN:
2666-2558
DOI:
10.2174/2666255813999200930163857
Language:
English
Publisher:
Bentham Science Publishers Ltd.
Publication Date:
2022
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