A Deep Learning Approach for Predicting Multiple Sclerosis
Edgar Rafael Ponce de Leon-Sanchez, Omar A. Domínguez-Ramírez, Ana M. Herrera-Navarro, Juvenal Rodríguez‐Reséndiz, Carlos Paredes-Orta, Jorge Domingo Mendiola-Santibañez
Abstract
This paper proposes a deep learning model based on an artificial neural network with a single hidden layer for predicting the diagnosis of multiple sclerosis. The hidden layer includes a regularization term that prevents overfitting and reduces the model complexity. The purposed learning model achieved higher prediction accuracy and lower loss than four conventional machine learning techniques. A dimensionality reduction method was used to select the most relevant features from 74 gene expression profiles for training the learning models. The analysis of variance test was performed to identify the statistical difference between the mean of the proposed model and the compared classifiers. The experimental results show the effectiveness of the proposed artificial neural network.