Tumor Prediction Using Microarray Gene Expression Profiles Through SVM and CBFS

The introduction to microarray technology has created a breakthrough in gene profiling which in turn has led to precise prediction of tumors. Microarray gene expression profiles are being used for classification of tumors into cancer-causing malignant and benign types. Furthermore, they are used to identify possible gene markers for each of the types in cases of malignant tumors, thus making a precise cancer diagnosis possible. This study mainly focuses on a supervised learning algorithm—support vector machine (SVM)—the use of various feature selection methods to enhance the precision score A forward greedy search strategy based on consistency and another known as the signal-to-noise ratio metric was used to discover the potential gene markers. According to the experiment findings, the consistency-based feature selection method is very effective for predicting cancer subtypes when combined with ISVMs. This would result in better prediction of cancer types based on the gene profiling data which would serve to be quite useful in the medical diagnosis of cancers.