In the literature there are multiple machine learning techniques that have been used successfully in clinical data analysis. However, there is little information about the parameter configurations, the required data transformations to prepare the data used to train and evaluate the models and the impact of these decisions in the accuracy of the predictive model. This research tackles these issues, using the clinical data of MIMICII to build features from physiological measure patterns to predict the decease of patients inside the hospital in the next 24 hours, building predictive models based on Logistic Regression, Neural Networks, Decision Trees and Nearest Neighbors. In particular, we use data associated to physiological measures of 3220 patients, where 2385 left the hospital alive and 835 passed in the hospital. The results show that the chosen strategy for building features from physiological data gives good results with Neural Networks and Logistic Regression with radial kernel models and the parameter configuration plays a fundamental role in the models performance.