Diabetes, a chronic metabolic disorder characterized by elevated blood sugar levels, is a significant global health concern. The management of diabetes often involves hospitalization, especially for individuals experiencing acute complications or undergoing significant treatment adjustments. One critical aspect of diabetes care is the prevention of readmission into a hospital within a short period after the initial discharge. Readmissions can indicate various issues, such as inadequate treatment during the initial hospitalization, complications arising post-discharge, or a lack of effective outpatient care.
The primary objective of this report is to develop a predictive model to anticipate whether a patient with diabetes will be readmitted to a hospital within 30 days following their initial discharge. Achieving this objective can offer several advantages, including improved patient care, reduced healthcare costs, and enhanced resource allocation within healthcare facilities. To accomplish our goal, we will employ logistic regression, a widely used statistical technique for binary classification. Logistic regression is well-suited for this task as it allows us to model the probability of readmission based on a set of relevant predictor variables. By analyzing these predictors, we can gain insights into the factors contributing to readmission risk among diabetic patients. Additionally, we will utilize imputation techniques to handle missing data, ensuring that our analysis is robust and representative of the patient population.