Original scientific paper
https://doi.org/10.32985/ijeces.14.3.2
Effective Prostate Cancer Detection using Enhanced Particle Swarm Optimization Algorithm with Random Forest on the Microarray Data
Sanjeev Prakashrao Kaulgud
; Department of Computer Science, Reva University and Presidency University, Bangalore, India.
Vishwanath Hulipalled
; Department of Computing and Information Technology, REVA University, Bengaluru, India
Siddanagouda Somanagouda Patil
; Department of Applied Maths & Computer Science, University of Agricultural Sciences, Bengaluru, India
Prabhuraj Metipatil
; Department of Computer Science & Engineering, REVA University, Bengaluru, India
Abstract
Prostate Cancer (PC) is the leading cause of mortality among males, therefore an effective system is required for identifying the sensitive bio-markers for early recognition. The objective of the research is to find the potential bio-markers for characterizing the dissimilar types of PC. In this article, the PC-related genes are acquired from the Gene Expression Omnibus (GEO) database. Then, gene selection is accomplished using enhanced Particle Swarm Optimization (PSO) to select the active genes, which are related to the PC. In the enhanced PSO algorithm, the interval-newton approach is included to keep the search space adaptive by varying the swarm diversity that helps to perform the local search significantly. The selected active genes are fed to the random forest classifier for the classification of PC (high and low-risk). As seen in the experimental investigation, the proposed model achieved an overall classification accuracy of 96.71%, which is better compared to the traditional models like naïve Bayes, support vector machine and neural network.
Keywords
Gene expression Omnibus; Particle Swarm Optimizer; Prostate Cancer; Random Forest;
Hrčak ID:
296689
URI
Publication date:
28.3.2023.
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