01454nas a2200229   4500000000100000000000100001008004100002260001200043653002100055653001300076653001300089100002200102700001800124700001800142700002600160245006700186856009500253300001000348490000600358520084600364022001401210       2018                            d  c03/201810aMachine Learning10aBig Data10ae-health1 aFrancisco Mochón1 aCarlos Elvira1 aAlberto Ochoa1 aJuan Carlos Gonzalvez00aMachine-Learning-Based No Show Prediction in Outpatient Visits  uhttp://www.ijimai.org/journal/sites/default/files/files/2017/03/ijimai_4_7_4_pdf_11885.pdf  a29-340 v43 aA recurring problem in healthcare is the high percentage of patients who miss their appointment, be it a consultation or a hospital test. The present study seeks patient’s behavioural patterns that allow predicting the probability of no- shows. We explore the convenience of using Big Data Machine Learning models to accomplish this task. To begin with, a predictive model based only on variables associated with the target appointment is built. Then the model is improved by considering the patient’s history of appointments. In both cases, the Gradient Boosting algorithm was the predictor of choice. Our numerical results are considered promising given the small amount of information available. However, there seems to be plenty of room to improve the model if we manage to collect additional data for both patients and appointments.  a1989-1660