Abstract
Abstract: Patients' readmission might be seen as a crucial aspect in lowering costs while maintaining high-quality patient care. As a result, anticipating and reducing readmission rates for patients will considerably enhance healthcare delivery. The goal of this research is to use machine learning algorithms to predict readmission of COPD (Chronic Obstructive Pulmonary Disease) patients. The major metrics for measuring models' prediction capability in each time frame were Area under Curve (AUC) and Accuracy (ACC). Then, the factors' relevance for each result was clearly recognized, and specified key variables were discriminated. With%91 ACC, our research had the best accuracy in predicting readmission
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