Avoiding Overfitting in Deep Neural Networks for Clinical Opinions Generation from General Blood Test Results

Abstract

We have used deep neural networks (DNNs) to generate clinical opinions from general blood test results. DNNs have overfitting problem in general. We believe the complex structure of DNN and insufficient data to be the major reasons of overfitting in our case. In this paper, we apply dropout and batch normalization to avoid overfitting. Experimental results show the improvement in the performance of the DNNs.