Open Access

ARTICLE

Optimized LSTM with Dimensionality Reduction Based Gene Expression Data Classification

S. Jacophine Susmi^*

University College of Engineering Tindivanam, Tindivanam, India

* Corresponding Author: S. Jacophine Susmi. Email:

Intelligent Automation & Soft Computing 2022, 33(2), 1139-1152. https://doi.org/10.32604/iasc.2022.023865

Received 24 September 2021; Accepted 30 November 2021; Issue published 08 February 2022

Abstract

The classification of cancer subtypes is substantial for the diagnosis and treatment of cancer. However, the gene expression data used for cancer subtype classification are high dimensional in nature and small in sample size. In this paper, an efficient dimensionality reduction with optimized long short term memory, algorithm (OLSTM) is used for gene expression data classification. The main three stages of the proposed method are explicitly pre-processing, dimensional reduction, and gene expression data classification. In the pre-processing method, the missing values and redundant values are removed for high-quality data. Following, the dimensional reduction is done by orthogonal locality preserving projections (OLPP). Finally, gene classification is done by an OLSTM classifier. Here the traditional long short term memory (LSTM) is modified using parameter optimization which uses the adaptive artificial flora optimization (AAFO) algorithm. Based on the migration and flora reproduction process, the AAFO algorithm is stimulated. Using the accuracy, sensitivity, specificity, precision, recall, and f-measure, the proposed performance is analyzed. The test outcomes illustrate the effectiveness of the gene expression data classification with a 94.19% of accuracy value. The proposed gene expression data classification is implemented in the MATLAB platform.

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