**1. Introduction**

The electronic health record (EHR) is an electronic version of patient's medical history and demographic, clinical and administrative data are included in them [1,2]. The EHR was created to improve the efficiency of health systems; however, it has several applications in clinical informatics and epidemiology. Specifically, EHR have been used for patient clustering, disease prediction and pattern recognition [3].

The analysis of clinical data associated to EHRs is based in statistical and Artificial Intelligence (AI) procedures. Recently, machine learning and deep learning algorithms have been successfully used to extract informative and useful patterns from the EHRs [4].

The present study is a continuation of previous work [5] in which EHRs were exploited to make predictions about patients with respiratory diseases. In this project, we propose the use of Machine Learning to predict the recurrence of patients with respiratory diseases in less than 6, 12 or 18 months (depending on diagnosis). For this task, four machine learning algorithms were used: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (kNN) and decision trees.
