Open Access

ARTICLE

An Intelligent Hybrid Ensemble Gene Selection Model for Autism Using DNN

by G. Anurekha^*, P. Geetha

Department of Information Science and Technology, College of Engineering, Anna University, Chennai, 600025, India

* Corresponding Author: G. Anurekha. Email:

Intelligent Automation & Soft Computing 2023, 35(3), 3049-3064. https://doi.org/10.32604/iasc.2023.029127

Received 25 February 2022; Accepted 05 May 2022; Issue published 17 August 2022

Abstract

Autism Spectrum Disorder (ASD) is a complicated neurodevelopmental disorder that is often identified in toddlers. The microarray data is used as a diagnostic tool to identify the genetics of the disorder. However, microarray data is large and has a high volume. Consequently, it suffers from the problem of dimensionality. In microarray data, the sample size and variance of the gene expression will lead to overfitting and misclassification. Identifying the autism gene (feature) subset from microarray data is an important and challenging research area. It has to be efficiently addressed to improve gene feature selection and classification. To overcome the challenges, a novel Intelligent Hybrid Ensemble Gene Selection (IHEGS) model is proposed in this paper. The proposed model integrates the intelligence of different feature selection techniques over the data partitions. In this model, the initial gene selection is carried out by data perturbation, and the final autism gene subset is obtained by functional perturbation, which reduces the problem of dimensionality in microarray data. The functional perturbation module employs three meta-heuristic swarm intelligence-based techniques for gene selection. The obtained gene subset is validated by the Deep Neural Network (DNN) model. The proposed model is implemented using python with six National Center for Biotechnology Information (NCBI) gene expression datasets. From the comparative study with other existing state-of-the-art systems, the proposed model provides stable results in terms of feature selection and classification accuracy.

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Keywords

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