Machine Learning Model for Cancer Diagnosis based on RNAseq Microarray

Abstract

Microarray technology is one of the most important recent breakthrough in experimental molecular biology. This novel technology for thousands of genes concurrently allows the supervising of expression levels in cells and has been increasingly used in cancer research to understand more of the molecular variations among tumors so that a more reliable classification becomes attainable. Machine learning techniques are loosely used to create substantial and precise classification models. In this paper, a function called Feature Reduction Classification Optimization (FeRCO) is proposed. FeRCO function uses machine learning techniques applied upon RNAseq microarray data for predicting whether the patient is diseased or not. The main purpose of FeRCO function is to define the minimum number of features using the most fitting reduction method along with classification technique that give the highest classification accuracy. These techniques include Support Vector Machine (SVM) both linear and kernel, Decision Trees, Random Forest, K-Nearest-Neighbors (KNN) and Naive Bayes (NB). Principle Component Analysis (PCA) both linear and kernel, Linear Discriminant Analysis (LDA) and Factor Analysis (FA) along with different machine learning techniques were used to find a low-dimensional subspace with better discriminative features for better classification. The major outcomes of this research can be considered as a roadmap for interesting researchers in that field to be able to choose the most suitable machine learning algorithm whatever classification or reduction.