Feature-based deep neural network approach for predicting mortality risk in patients with COVID-19

In this study, we integrate deep neural network (DNN) with hybrid approaches (feature selection and instance clustering) to build prediction models for predicting mortality risk in patients with COVID-19. Besides, we use cross-validation methods to evaluate the performance of these prediction models, including feature based DNN, cluster-based DNN, DNN, and neural network (multi-layer perceptron). The COVID-19 dataset with 12,020 instances and 10 cross-validation methods are used to evaluate the prediction models. The experimental results showed that the proposed feature based DNN model, holding Recall (98.62%), F1-score (91.99%), Accuracy (91.41%), and False Negative Rate (1.38%), outperforms than original prediction model (neural network) in the prediction performance. Furthermore, the proposed approach uses the Top 5 features to build a DNN prediction model with high prediction performance, exhibiting the well prediction as the model built by all features (57 features). The novelty of this study is that we integrate feature selection, instance clustering, and DNN techniques to improve prediction performance. Moreover, the proposed approach which is built with fewer features performs much better than the original prediction models in many metrics and can still remain high prediction performance.