Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care

Published on Dec 1, 2020in International Journal of Electrical and Computer Engineering
· DOI :10.11591/IJECE.V10I6.PP6655-6663
Rafid Sagban5
Estimated H-index: 5
(University of Babylon),
Haydar Abdulameer Marhoon2
Estimated H-index: 2
,
Raaid Alubady4
Estimated H-index: 4
(University of Babylon)
Source
Abstract
Rule-based classification in the health field using artificial intelligence went away to rendering solutions in decision-making problems in different domains. The most important of these challenges is access to good and fast health facilities, which pose a major threat to injure the disease. Cervical   cancer is one of the most frequent causes of death to the female. The diagnosis methods for cervical cancer used in health centers are costly and time-consuming. In this paper, Bat Algorithm for Feature Selection and Ant Colony Optimization based classification algorithm applied on cervical cancer data set was obtained from the repository of the University of California, Irvine to analyze the disease based on the optimal features. The proposed algorithm outperforms the other methods in terms of comprehensibility and obtains a better result in terms of classification accuracy.
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