Gene Selection Approach Utilizing Data Clustering Based Technique Optimization for Tumor Classification

This paper introduces an advanced approach to classify tumor type by microarray gene selection records. The method utilizes gene selection based on shuffling in connection with optimized data clustering. Merging Artificial Bee Colony (ABC) with genetic algorithm (GA) as a clustering tool to choose the key genes develops a new hybrid algorithm, ABC-GA. Support Vector Machine recursive feature elimination (SVM-RFE) and Multilayer Perceptron (MLP) artificial neural networks were used to enhance accuracy. Nonetheless, outcomes show that using shuffling in clustering strengthen classification accuracy significantly. The suggested algorithm (ABC-GA) performs better than Swarm optimization technique (PSO) in reaching good classification results. Better precision has been achieved using (SVM-RFE) classifier against MLP