A Hybrid Metaheuristics based technique for Mutation Based Disease Classification

Abstract

Due to recent advancements in computational biology, DNA microarray technology has evolved as a useful tool in the detection of mutation among various complex diseases like cancer. The availability of thousands of microarray datasets makes this field an active area of research. Early cancer detection can reduce the mortality rate and the treatment cost. Cancer classification is a process to provide a detailed overview of the disease microenvironment for better diagnosis. However, the gene microarray datasets suffer from a curse of dimensionality problems also the classification models are prone to be overfitted due to small sample size and large feature space. To address these issues, the authors have proposed an Improved Binary Competitive Swarm Optimization Whale Optimization Algorithm (IBCSOWOA) for cancer classification, in which IBCSO has been employed to reduce the informative gene subset originated from using minimum redundancy maximum relevance (mRMR) as filter method. The IBCSOWOA technique has been tested on an artificial neural network (ANN) model and the whale optimization algorithm (WOA) is used for parameter tuning of the model. The performance of the proposed IBCSOWOA is tested on six different mutation-based microarray datasets and compared with existing disease prediction methods. The experimental results indicate the superiority of the proposed technique over the existing nature-inspired methods in terms of optimal feature subset, classification accuracy, and convergence rate. The proposed technique has illustrated above 98% accuracy in all six datasets with the highest accuracy of 99.45% in the Lung cancer dataset.