MicroRNA expression classification for human disease prediction

Recent research has shown that microRNA plays an important role in human disease specification. Study of miRNA expression helps to accelerate the diagnosis of diseases and to anticipate treatment. However experimental identification of diseases from microRNA expression such as RPM poses difficulties. Nowadays, we haven't enough bioinformatics algorithm to predict the association between miRNA and diseases. Herein, we present a machine learning based approach for distinguishing patient from normal person based on miRNA expression. In this paper, we compare different machine learning algorithms such as SVM, KNN and logistic regression to predict infected gene from miRNAs RPM values.