Microarray Prostate Cancer Classification using Eminent Genes

Prostate cancer is a common type and it ranks second among men. Certain kinds of prostate cancers remain lethargic and don't lead to cancer death, even though lacking treatment. Though, a substantial amount of people with this cancer has violent lumps that advances quickly to neoplasm disease and remain very risky and hence they should be accurately classified. Presently, classification results for prostate patients are directed by numerous machine learning algorithms. The foremost goal of this research is to classify the prostate cancer in a precise way. Publicly available two class prostate data that contains 12,600 genes and 136 samples is considered in this paper. Initially Kruskal-Wallis test along with Principal Component Analysis (PCA) is applied to discover the most informative genes. Then those genes are classified using Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB) and bagging ensemble KNN classifiers for classifying prostate patients as either normal or abnormal. The results indicate that a maximum overall accuracy of 96.46% is achieved for abnormal prostate data samples and 95.78% for normal prostate data samples using XGB classifier when compared with other types of classifiers considered in this paper.