Hybrid feature selection of microarray prostate cancer diagnostic system
Nursabillilah Mohd Ali, Ainain Nur Hanafi, Mohd Safirin Karis, Nur Hazahsha Shamsudin, Ezreen Farina Shair, Nor Hidayati Abdul Aziz
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.
Keywords
Genetic algorithm; Particle swarm optimization; Relief-F; Support vector machine; Whale optimization algorithm
DOI:
http://doi.org/10.11591/ijeecs.v36.i3.pp1884-1894
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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).
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