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

• Published in: IEEE access Vol. 8; pp. 127462 - 127476
• Main Authors: Prabhakar, Sunil Kumar, Lee, Seong-Whan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Classification; Feature extraction; Feature selection; Gene expression; Optimization; Optimization algorithms; Optimization techniques; Prostate cancer; Signal to noise ratio; Walnuts; Wavelet transforms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3006197

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 Naïve Bayesian Classifier (NBC).