Gene Expression Analysis for Early Lung Cancer Prediction Using Machine Learning Techniques: An Eco-Genomics Approach

Cancer as a multifactorial disorder develops due to the complex interaction between gene and environment. A person may be susceptible to cancer due to his individual genetic makeup. Cancer causes maximum death worldwide as per data given by the World Health Organization. Some cases are reported with particular genetic makeup. Hence, the proper understanding of eco-genomics of cancer is necessary to interpret the underlying cause and risk factor for cancer. Combining huge gene expression date available in cyberspace and advanced machine learning technique may bring out the strongest candidate genes, which individually or as a part of the complex system, have more accurate prognostic value to determine someone’s susceptibility toward cancer. In this paper, we have analyzed gene expression data for the lung cancer available in the Kent Ridge Bio-Medical Dataset Repository. The microarray gene expression data are analyzed to select and predict the optimal subset of genes, which are the most probable causing agent of lung cancer.