Oral epithelial dysplasia (OED) often precedes oral cancer. Understanding the underlying complex biological aspects of dysplasia associated oral carcinogenesis using important gene sets is thus important. Computation assisted gene set identification through different feature ranking and visualization techniques was therefore attempted in this study. Result suggested that, weighted support vector machine (SVM) could be useful for feature ranking and SVM for attribute selection. Alteration in keratinization, cell-cell communication and peptidase activity was the major affected phenomena, while extracellular matrix dynamics was also found to be hampered. During best gene subset identification, set of six genes could classify normal (NOM) and oral squamous cell carcinoma (OSCC) conditions and two sets comprising four genes in each could classify NOM and dysplastic (DYS) conditions with 100% sensitivity and specificity. A gene set, comprising 32 genes showed best efficacy of 94.12% sensitivity, 99.40% specificity and 98.89% accuracy during classification of DYS and OSCC. © 2016 Published by Elsevier Inc.