RESUMEN
In this work, we compare the performance of a multilayer perceptron neural network and convolutional networks for the prediction of 14-day mortality in patients with TBI, using a database obtained in a low-and middle-income country, with 529 records and 16 predictor variables. The missing values of several variables were filled in with techniques such as decision tree, random forest, k-nearest-neighbor and linear regression. In the simulation of neural networks, several optimization methods were used, such as RMSProp, Adam, Adamax and SGDM. The best results obtained for the prediction rate were an accuracy of 0.845 and an area under the ROC curve of 0.911. Clinical Relevance- This proposes the prediction of early mortality in patients with TBI with an area under ROC curve of 0.911.