Clinical Opinions Generation from General Blood Test Results Using Deep Neural Network with Principle Component Analysis and Regularization

Abstract

The conventional approach of generating clinical opinions from general blood test (GBT) results uses the deep neural network (DNN) comprised of fully-connected layers. The large number of input neurons and output neurons result in the complex DNN structure, which causes overfitting problem. However, the dimension of the input vector and the output vector cannot be reduced arbitrarily, as all GBT results and all clinical opinions should be retained. In order to avoid overfitting, we apply principal component analysis (PCA) and parameter regularization. PCA is a dimensionality reduction technique which may be used to reduce the number of input neurons, minimizing the information loss. Besides, we apply L1 penalty or L2 penalty to the loss function of the DNN to apply parameter regularization. We also apply PCA and the regularization simultaneously. Experimental results show that all three proposed methods outperform the conventional DNN, and applying only L1-regularization shows the best performance in avoiding overfitting in the DNN for generating clinical opinions.