Web-Based Application for Accurately Classifying Cancer Type from Microarray Gene Expression Data Using a Support Vector Machine (SVM) Learning Algorithm

Intelligent optimization algorithms have been widely used to deal complex nonlinear problems. In this paper, we have developed an online tool for accurate cancer classification using a SVM (Support Vector Machine) algorithm, which can accurately predict a lung cancer type with an accuracy of approximately 95%. Based on the user specifications, we chose to write this suite in Python, HTML and based on a MySQL relational database. A Linux server supporting CGI interface hosts the application and database. The hardware requirements of suite on the server side are moderate. Bounds and ranges have also been considered and needs to be used according to the user instructions. The developed web application is easy to use, the data can be quickly entered and retrieved. It has an easy accessibility through any web browser connected to the firewall-protected network. We have provided adequate server and database security measures. Important notable advantages of this system are that it runs entirely in the web browser with no client software need, industry standard server supporting major operating systems (Windows, Linux and OSX), ability to upload external files. The developed application will help researchers to utilize machine learning tools for classifying cancer and its related genes.