02504nas a2200241   4500000000100000000000100001008004100002260001200043653003900055653002500094653001800119653002100137653001900158100001300177700001200190700001700202245005800219856008000277300000900357490000600366520187600372022001402248       2021                            d  c06/202110aConvolutional Neural Network (CNN)10aCoronavirus COVID-1910aDeep Learning10aMachine Learning10aMedical Images1 aJ. Prada1 aY. Gala1 aA. L. Sierra00aCOVID-19 Mortality Risk Prediction Using X-Ray Images  uhttps://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_1.pdf  a7-140 v63 aThe pandemic caused by coronavirus COVID-19 has already had a massive impact in our societies in terms of health, economy, and social distress. One of the most common symptoms caused by COVID-19 are lung problems like pneumonia, which can be detected using X-ray images. On the other hand, the popularity of Machine Learning models has grown exponentially in recent years and Deep Learning techniques have become the state-of-the-art for image classification tasks and is widely used in the healthcare sector nowadays as support for clinical decisions. This research aims to build a prediction model based on Machine Learning, including Deep Learning, techniques to predict the mortality risk of a particular patient given an X-ray and some basic demographic data. Keeping this in mind, this paper has three goals. First, we use Deep Learning models to predict the mortality risk of a patient based on this patient X-ray images. For this purpose, we apply Convolutional Neural Networks as well as Transfer Learning techniques to mitigate the effect of the reduced amount of COVID19 data available. Second, we propose to combine the prediction of this Convolutional Neural Network with other patient data, like gender and age, as input features of a final Machine Learning model, that will act as second and final layer. This second model layer will aim to improve the goodness of fit and prediction power of our first layer. Finally, and in accordance with the principle of reproducible research, the data used for the experiments is publicly available and we make the implementations developed easily accessible via public repositories. Experiments over a real dataset of COVID-19 patients yield high AUROC values and show our two-layer framework to obtain better results than a single Convolutional Neural Network (CNN) model, achieving close to perfect classification.  a1989-1660