Evaluating Disease Prognosis in Hospital Readmission Using Machine Learning Techniques

This research study investigates the prognostic value of cardiac disease, Body Mass Index (BMI), diabetes, and hypertension in relation to hospital readmissions. The study aims to develop predictive models that can identify high-risk individuals and reduce the financial burden associated with readmissions. This study is conducted using electronic health records of individuals with a history of hypertension, heart disease, or both. The relationship between these factors and hospital readmissions within post-discharge was analyzed using ensemble learning techniques. The findings demonstrate that elevated BMI and glucose levels are significant predictors of hospital readmissions, independent of hypertension and cardiac disease status. These results highlight the importance of addressing these factors in preventing readmissions and improving patient outcomes. This research contributes to the development of effective strategies for reducing hospital readmissions and improving the overall quality of care for patients with cardiovascular conditions.