Predicting Cancer Risk from Genome Data: A Multilayer Perceptron Approach

Abstract

This paper proposes a deep learning method to predict cancer risk from gene symbols using a multilayer perceptron (MLP) feed forward neural network. The paper uses a data set of gene symbols and their corresponding cancer risk labels, obtained from a DNA microarray analysis. The paper then builds and compares different machine learning models, such as logistic regression, linear discriminant analysis, quadratic discriminant analysis, decision tree classifier, gaussian nb, ada boost classifier, gaussian process classifier, support vector machine, and random forest. Deep learning MLP model is built, tuned and optimized for hyperparameters which improves the accuracy significantly by 9.09% compared to the best machine learning model. The paper evaluates the performance of the MLP model on the data set using accuracy, precision, recall, and F1-score metrics. This paper contributes to the field of machine learning and bioinformatics by providing a novel and effective way to predict cancer risk from gene symbols.