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

Abstract

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.

Review on Hybrid Swarm Algorithms for Feature Selection

“…The literature review that was introduced in the previous section showed that the hybrid swarm algorithms used for feature selection can enhance classification accuracy [23]. [20][21][22][24][25], [29], and [31][32] achieved accuracy between [90%-99%] while the other research achieved less than 90% classification accuracy. Table 3 represents a comparative study of classification accuracy between various feature selection techniques and the hybrid method of different swarm algorithms for feature selection.…”

“…In 2020, Sagban et al [24] utilized the Binary BAT algorithm to optimize feature selection by benefiting from frequency tuning and automatic zooming of the algorithm. They then attempted to do classification using the Ant-Miner classifier with five folds, each using 20% of the test data.…”

An overlapping conscious relief-based feature subset selection method

“…Instead of 𝑝(𝐼 𝑛 ), we are more interested in approximating the posterior 𝑝(𝑦 𝑛 |𝐼 𝑛 ) where 𝑦 𝑛 is the actual class label of 𝐼 𝑛 . This posterior gives the assurance of similar data points belonging to the same class which can be expressed using (2).…”

Hybrid filtering methods for feature selection in high-dimensional cancer data

“…Several studies on high-dimensional classification reporting methods have been published in the literature. Liang et al [1] proposed conditional mutual information-based feature selection with interaction to reduce performance error [2]. Tally et al [3] discovered the genetic algorithm feature selection with a support vector machine classifier for intrusion detection, while Sagban et al [2] investigated the performance of feature selection applied to cervical cancer data.…”