| Summary: | Ovarian Cancer is a type of cancer that begins in ovaries posing a serious threat to women. As a result, it leads to abnormal cells which has the ability to spread to other regions of the body. A highly useful diagnostic and prognostic data for ovarian cancer research is provided by the microarray data. Typically, genes with tens of thousands of dimension are present in the microarray data of ovarian cancer. There is a systematic methodology required to analyze this data and so it is important to select the most important genes or features for the entire data to avoid the computational complexity. In this work, an integrated approach to feature selection is done by two consecutive steps. Initially, the features are selected by the standard gene selection techniques such as Correlation Coefficient, T-Statistics and Kruskal-Wallis test. The selected genes or features will be further optimized by four suitable stochastic optimization algorithms chosen here such as Central Force Optimization (CFO), Lightning Attachment Procedure Optimization (LAPO), Genetic Bee Colony Optimization (GBCO) and Artificial Algae Optimization (AAO). Finally, it is classified with five different classifiers to analyze the ovarian cancer classification and the best results are projected when Kruskal Wallis test with GBCO is conducted and classified with Support Vector Machine - Radial Basis Function (SVM-RBF) Kernel technique giving a high classification accuracy of 99.48%. Similar results are also obtained when Correlation Coefficient test with AAO is conducted and classified with Logistic Regression giving a high classification accuracy of 99.48%.
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