SVM-ABC based cancer microarray (gene expression) hybrid method for data classification

Microarray technology presents a challenge due to the large dimensionality of the data, which can be difficult to interpret. To address this challenge, the article proposes a feature extraction-based cancer classification technique coupled with artificial bee colony optimization (ABC) algorithm. The ABC-support vector machine (SVM) method is used to classify the lung cancer datasets and compared them with existing techniques in terms of precision, recall, F-measure, and accuracy. The proposed ABC-SVM has the advantage of dealing with complex nonlinear data, providing good flexibility. Simulation analysis was conducted with 30% of the data reserved for testing the proposed method. The results indicate that the proposed attribute classification technique, which uses fewer genes, performs better than other modalities. The classifiers, such as naïve Bayes, multi-class SVM, and linear discriminant analysis, were also compared and the proposed method outperformed these classifiers and state-of-the-art techniques. Overall, this study demonstrates the potential of using intelligent algorithms and feature extraction techniques to improve the accuracy of cancer diagnosis using microarray gene expression data.