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

Rafid Sagban, Haydar A. Marhoon, Raaid Alubady


Rule-based classification in the field of health care using artificial intelligence provides solutions in decision-making problems involving different domains. An important challenge is providing access to good and fast health facilities. Cervical cancer is one of the most frequent causes of death in females. The diagnostic 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 were applied on cervical cancer data set obtained from the repository of the University of California, Irvine to analyze the disease based on optimal features. The proposed algorithm outperforms other methods in terms of comprehensibility and obtains better results in terms of classification accuracy.


Feature Selection; Detection Making; Bat Algorithm; Ant Colony Optimization; Rule-based Classification

Full Text:


DOI: http://doi.org/10.11591/ijece.v10i6.pp6655-6663

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578