A novel hybrid algorithm based on Harris Hawks for tumor feature gene selection

Abstract

Background Gene expression data are often used to classify cancer genes. In such high-dimensional datasets, however, only a few feature genes are closely related to tumors. Therefore, it is important to accurately select a subset of feature genes with high contributions to cancer classification. Methods In this article, a new three-stage hybrid gene selection method is proposed that combines a variance filter, extremely randomized tree and Harris Hawks (VEH). In the first stage, we evaluated each gene in the dataset through the variance filter and selected the feature genes that meet the variance threshold. In the second stage, we use extremely randomized tree to further eliminate irrelevant genes. Finally, we used the Harris Hawks algorithm to select the gene subset from the previous two stages to obtain the optimal feature gene subset. Results We evaluated the proposed method using three different classifiers on eight published microarray gene expression datasets. The results showed a 100% classification accuracy for VEH in gastric cancer, acute lymphoblastic leukemia and ovarian cancer, and an average classification accuracy of 95.33% across a variety of other cancers. Compared with other advanced feature selection algorithms, VEH has obvious advantages when measured by many evaluation criteria.

A new parallel multi-objective Harris hawk algorithm for predicting the mortality of COVID-19 patients

“…In this study, we propose a novel parallel multi-objective HHO method (PHHO-KNN) using K-nearest neighbour (KNN) classifiers for predicting the mortality of COVID-19 patients according to their findings (features). Liu et al (2023) proposed a new hybrid HHO algorithm for tumour feature gene selection. They evaluated each gene through the variance filter and selected the best feature genes.…”