  In this paper, we propose a novel method named CNN-AE to predict survival
chance of COVID-19 patients using a CNN trained on clinical information. To
further increase the prediction accuracy, we use the CNN in combination with an
autoencoder. Our method is one of the first that aims to predict survival
chance of already infected patients. We rely on clinical data to carry out the
prediction. The motivation is that the required resources to prepare CT images
are expensive and limited compared to the resources required to collect
clinical data such as blood pressure, liver disease, etc. We evaluate our
method on a publicly available clinical dataset of deceased and recovered
patients which we have collected. Careful analysis of the dataset properties is
also presented which consists of important features extraction and correlation
computation between features. Since most of COVID-19 patients are usually
recovered, the number of deceased samples of our dataset is low leading to data
imbalance. To remedy this issue, a data augmentation procedure based on
autoencoders is proposed. To demonstrate the generality of our augmentation
method, we train random forest and Na\"ive Bayes on our dataset with and
without augmentation and compare their performance. We also evaluate our method
on another dataset for further generality verification. Experimental results
reveal the superiority of CNN-AE method compared to the standard CNN as well as
other methods such as random forest and Na\"ive Bayes. COVID-19 detection
average accuracy of CNN-AE is 96.05% which is higher than CNN average accuracy
of 92.49%. To show that clinical data can be used as a reliable dataset for
COVID-19 survival chance prediction, CNN-AE is compared with a standard CNN
which is trained on CT images.
