Development of predictive models for cervical cancer based on gene expression profiling data

Abstract

Abstract Cervical cancer and the prediction of clinical outcome are among the most important emerging applications of gene expression microarray technology with feature sequencing of microRNA. By using reliable and dependable classification of machine learning algorithms available for microarray gene expression profiling data is the key in order to develop the most suitable and possible predictive model to be used by patient. In this paper, two-machine learning algorithms have been used which are Support Vector Machine (SVM) and Random Forests (RF) for the predictive models of cervical cancer. We identify and evaluate the performance of these two algorithms in order to know which algorithm has better performance. In this study, 714 features and 58 samples are used to develop predictive model for cervical cancer and our computational results show that RF algorithm outperform SVM algorithm with the accuracy of 94.21%. Our data also underline the importance of variables, which give the significant role in predicting the occurrence of cervical cancer.