Transformation Based Tri-Level Feature Selection Approach Using Wavelets and Swarm Computing for Prostate Cancer Classification

Sunil Kumar Prabhakar,Seong-Whan Lee

doi:10.1109/access.2020.3006197

Sunil Kumar Prabhakar, Seong-Whan Lee

Open Access

https://doi.org/10.1109/access.2020.3006197

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Abstract

Prostate Cancer is a cancer that occurs in the prostate- a small walnut shaped gland in men. This gland helps in the production of seminal fluid which is used to nourish and transport the sperm. One of the most common types of cancer in men is prostate cancer. A microarray dataset contains the microarray gene expression information. On a genome wide scale, gene expression profiles make it easy to analyze the patterns between genes and cancers, however the analysis of gene expression data is very difficult as it has a high dimensionality and low Signal to Noise Ratio (SNR). In this paper, a transformation-based Tri-level feature selection using wavelets for prostate cancer classification has been proposed. For the input microarray data, initially wavelets are applied and then the essential features are selected. Then the standardized gene selection techniques are implemented such as Relief-F, Fishers Score, Information Gain and SNR for a second level feature selection stage. Finally, before proceeding to classification, a third level feature selection by means of optimization techniques are implemented. The optimization techniques incorporated in this work are Marriage in Honey Bee Optimization Algorithm (MHBOA), Migrating Birds Optimization Algorithm (MBOA), Salp Swarm Optimization Algorithm (SSOA) and Whale Optimization Algorithm (WOA). This kind of an approach is totally new, and the best results show when SNR with WOA is classified with Artificial Neural Network (ANN) giving a classification accuracy of 99.48%. The second highest classification accuracy of 99.22% is obtained when Relief-F test with MBOA is classified with Naive Bayesian Classifier (NBC).

Highlights

The prostate is a very small walnut shaped gland present in the pelvis of men [1]
This work deals with the classification of specific microarray data into normal or abnormal based on Discrete Wavelet Transform (DWT)
Test with Marriage in Honey Bee Optimization Algorithm (MHBOA) is classified with Artificial Neural Network (ANN), Fishers Score with Salp Swarm Optimization Algorithm (SSOA) when classified with ANN, Signal to Noise Ratio (SNR) with SSOA when classified with Linear Discriminant Analysis (LDA) and IG with SSOA when classified with Support Vector Machine (SVM)-RBF

Summary

Introduction

The prostate is a very small walnut shaped gland present in the pelvis of men [1]. By getting a digital rectal exam, the prostate can be examined as it is located next to the bladder. A form of cancer that develops in the prostate gland is called prostate cancer [2]. Prostate cancer grows slowly and is confined to the prostate gland alone. Some kinds of prostate cancer can require a minimal treatment while others can spread very quickly and aggressively. While confined to the prostate gland and if it detected earlier, the chances are better for a successful treatment [3]. The common risk factors of this disease include age, ethnicity, family history, The associate editor coordinating the review of this manuscript and approving it for publication was Nadeem Iqbal

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