A Hybrid Feature Selection Method for Improve the Accuracy of Medical Classification Process
Maria Mohammad Yousef
Maria Mohammad Yousef*, Department of Computer Science, Al al-Bayt University, Jordan
Manuscript received on November 13, 2021. | Revised Manuscript received on November 20, 2021. | Manuscript published on November 30, 2021. | PP: 50-55 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.A96241111121 | DOI: 10.35940/ijitee.A9624.1111121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Generally, medical dataset classification has become one of the biggest problems in data mining research. Every database has a given number of features but it is observed that some of these features can be redundant and can be harmful as well as disrupt the process of classification and this problem is known as a high dimensionality problem. Dimensionality reduction in data preprocessing is critical for increasing the performance of machine learning algorithms. Besides the contribution of feature subset selection in dimensionality reduction gives a significant improvement in classification accuracy. In this paper, we proposed a new hybrid feature selection approach based on (GA assisted by KNN) to deal with issues of high dimensionality in biomedical data classification. The proposed method first applies the combination between GA and KNN for feature selection to find the optimal subset of features where the classification accuracy of the k-Nearest Neighbor (kNN) method is used as the fitness function for GA. After selecting the best-suggested subset of features, Support Vector Machine (SVM) are used as the classifiers. The proposed method experiments on five medical datasets of the UCI Machine Learning Repository. It is noted that the suggested technique performs admirably on these databases, achieving higher classification accuracy while using fewer features.
Keywords: Dimensionality Problem, Feature Selection, classification, Genetic Algorithm.