Classifying High Risk Diabetic Patients using Supervised Machine Learning Models
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
How to Cite?: Ritambhara Jha, "Classifying High Risk Diabetic Patients using Supervised Machine Learning Models", Volume 12 Issue 12, December 2023, International Journal of Science and Research (IJSR), Pages: 806-811, https://www.ijsr.net/getabstract.php?paperid=SR231209033422, DOI: https://dx.doi.org/10.21275/SR231209033422