Description

Patients’ readmission can be considered as a critical factor affecting cost reduction while maintaining a high quality treatment of patients. Therefore, predicting and controlling patients’ readmission rates would significantly improve the healthcare service. In this study, we aim at predicting the readmission of COPD (Chronic Obstructive Pulmonary Disease) patients through the deployment of machine learning algorithms. Accuracy (ACC) were considered as the main criteria for evaluating models’ prediction power in each time frame. Then, the importance of the variables for each outcome was explicitly identified, and defined important variables have then been differentiated. Here, we have used Decision Tree, Random Forest and ANN algorithm for predicting and analysis.