In this work, we apply a two-step approach for classifying the hospital readmission from patient information such as race, gender, age, medications, number of tests using random forests classifier [1]. In this approach, we first classify whether a patient will be readmitted or not, and then we classify, given Time period, whether a patient will be readmitted within that time period after that time period from the estimated readmitted patient. Hence, Random forests are a supervised learning algorithm that produce multiple unbiased classification trees from the random samples of the training data with replacement in which each classification tree votes for a class and the forest picks the classification with the highest votes over all the classification
