Classifying High Risk Diabetic Patients using Supervised Machine Learning Models

Ritambhara Jha

Abstract: Hospital re - admissions are a major healthcare concern, primarily in terms of the quality services delivered to hospitalized patients and the accompanying healthcare expenditures. In 2018, 20 percent of adult hospital re - admissions were associated with four conditions at index admission: septicemia, heart failure, diabetes, and chronic obstructive pulmonary disease (COPD) [10]. Our goal is to examine these studies and focus on the frequency of re - admissions, their causes and their usefulness as a measure of care quality. This paper compares four popular approaches in the literature to classify the diabetic patients into Re - admissible or Non - Re - admissible.

Keywords: Classification ML models, Hospital Re - admissions, Healthcare Expenditures, Care Quality, Identifying Diabetic Patients