Junhyeok Park, Songwook Lee

KIPS Transactions on Software and Data Engineering, Vol. 9, No. 10, pp. 309-316, Oct. 2020
https://doi.org/10.3745/KTSDE.2020.9.10.309,   PDF Download:
Keywords: Mortality Prediction, Convolutional Neural Network, Random Forest, Feature selection, Deep Learning
Abstract

We predict the mortality of the elderly patients visiting the emergency department who are over 65 years old using Feed Forward Neural Network (FFNN) and Convolutional Neural Network (CNN) respectively. Medical data consist of 99 features including basic information such as sex, age, temperature, and heart rate as well as past history, various blood tests and culture tests, and etc. Among these, we used random forest to select features by measuring the importance of features in the prediction of mortality. As a result, using the top 80 features with high importance is best in the mortality prediction. The performance of the FFNN and CNN is compared by using the selected features for training each neural network. To train CNN with images, we convert medical data to fixed size images. We acquire better results with CNN than with FFNN. With CNN for mortality prediction, F1 score and the AUC for test data are 56.9 and 92.1 respectively.

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