A Hybrid Neural Network Approach for Lung Cancer Classification with Gene Expression Dataset and Prior Biological Knowledge

Abstract

Lung cancer has continued to be the leading cause of related mortality and its frequency is rising daily worldwide. A reliable and accurate classification is essential for successful lung cancer diagnosis and treatment. Gene expression microarray, which is a high-throughput platform, makes it possible to discover genomic biomarkers for cancer diagnosis and prognosis. This study proposes a new approach of using improved Particle Swarm Optimization (IMPSO) technique to improve the Multi-Layer Perceptrons (MLP) neural network prediction accuracy. The MLP weights and biases are computed by the IMPSO for more accurate lung cancer prediction. The proposed discriminant method (MLP-IMPSO) integrates the prior knowledge of lung cancer classification on the basis of gene expression data to enhance the classification accuracy. Evaluations and comparisons of prediction performance were thoroughly carried out between the proposed model and the representative machine learning methods (support vector machine, MLP, radial basis function neural network, C4.5, and Naive Bayes) on real microarray lung cancer datasets. The cross-data set validations made the assessment reliable. The performance of the proposed approach was better upon the incorporation of prior knowledge. We succeeded in demonstrating that our method improves lung cancer diagnosis accuracy with prior biological knowledge. The evaluation results also showed the effectiveness the proposed approach for lung cancer diagnosis.