Abstract
Hospital readmission systems increase the efficiency of initial treatment at hospitals. This chapter proposes a novel prediction model for identifying risk factors using machine learning techniques, and the proposed model is tested using 10-fold cross-validation for generalization and finds hidden patterns in the diagnosis, medications, lab test results, and basic characteristics of patients related to readmissions. This model predicts a statistically problem solving using searching patterns. Based on the findings of this study, for the given dataset, pruning dataset manifested the most accurate prediction of readmissions to the hospital with 94.8% accuracy for patients admitted in a year.