Hybrid feature selection of microarray prostate cancer diagnostic system

Mohd Ali, Nursabillilah and Hanafi, Ainain Nur and Karis, Mohd Safirin and Shamsudin, Nur Hazahsha and Shair, Ezreen Farina and Abdul Aziz, Nor Hidayati (2024) Hybrid feature selection of microarray prostate cancer diagnostic system. Indonesian Journal Of Electrical Engineering And Computer Science, 36 (3). pp. 1884-1894. ISSN 2502-4752

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Abstract

DNA microarray prostate cancer diagnosis systems are widely used, and hybrid feature selection methods are applied to select optimal features to address the high dimensionality of the dataset. This work proposes a new hybrid feature selection method, namely the relief-F (RF)-genetic algorithm (GA) with support vector machine (SVM) classification method. The aim is to evaluate the performance of the proposed method in terms of accuracy, computation time, and the number of selected features. The method is implemented using Python in PyCharm and is evaluated on a DNA microarray prostate cancer. The outcome of this work is a performance comparison table for the proposed methods on the dataset. The performance of GA, particle swarm optimization (PSO), and whale optimization algorithm (WOA) is compared in terms of accuracy, computation time, and the number of selected features. Results show that GA has the highest average accuracy (91.17%) compared to PSO (90.52%) and WOA (85.74%). GA outperforms PSO and WOA due to its superior convergence properties and better alignment with complex problems.

Item Type:	Article
Uncontrolled Keywords:	Genetic algorithm, Particle swarm optimization, Relief-F, Support vector machine, Whale optimization algorithm
Divisions:	Faculty Of Electrical Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	06 Jan 2025 11:39
Last Modified:	06 Jan 2025 11:39
URI:	http://eprints.utem.edu.my/id/eprint/28196
Statistic Details:	View Download Statistic

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