DNA Microarray Gene Expression Analysis for Diagnosis of Oral Dysplasia and Squamous-Cell Carcinoma

Abstract

A tremendous amount of research has investigated carcinoma and looked into treatments to lower cancer mortality. Nevertheless, oral cancer is still a considerable health issue worldwide and mortality and incidence rates are rising. Oral cancer is ranked as the sixth most common carcinoma and is a prime health dilemma globally. Thus, it is increasingly important to have tools that allow for discrimination between oral dysplasia and squamous-cell carcinoma. Although conventional methods such as medical imaging can be helpful in prediction and diagnosis of oral cancer, these methods continue to have limitations. More recently, machine learning has been used as a complementary approach in biomedical research and it now plays a leading role in the emerging domains of computational biology and bioinformatics. Specifically, DNA chip gene expression technology is currently helping researchers to distinguish oral dysplasia and squamous-cell carcinoma thru analysis of gene expression in tissue samples. Nonetheless, one of the prime challenges in the computational process of microarray data is the curse of dimensionality due to the crushing number of features. Therefore, we applied a random projection (RP) feature construction technique to tackle the problem of high-dimensional gene expression data and to increase the efficiency of our proposed model. In addition, we combined a RP technique with a support vector machine (SVM) that employs a sequential minimal optimization training algorithm (SMO) in order to efficiently differentiate squamous-cell carcinoma and oral dysplasia. The highest classification accuracy recorded by our proposed model was 95.6332%. We show in this study that using a SMO machine-learning classifier with a RP dimensionality reduction tool can be effective for classifying oral cancer.