Predicting re-admission to hospital for diabetes treatment: a machine learning solution Online publication date: Wed, 31-Mar-2021
by Satish M. Srinivasan; Yok-Fong Paat; Philmore Halls; Ruth Kalule; Thomas E. Harvey
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 13, No. 5/6, 2020
Abstract: Predictive analytics embrace an extensive range of techniques for identifying patterns within data to predict future outcomes and trends. The objective of this study is to design and implement a predictive analytics system that can be used to forecast the likelihood that a diabetic patient will be readmitted to the hospital. Using the Diabetes 130-US hospitals dataset we modelled the relationship between the patient re-admission (predictor) and the response variable using the Random Forest classifier. We obtained a maximum AUC of 0.684 and an F1 Score of 52.07%. Our study reveals that attributes such as number of inpatient visits, discharge disposition, admission type, and number of laboratory tests are strong predictors for the re-admission of patients. Findings from this study can help hospitals design suitable protocols to ensure that patients with a higher probability of re-admission are recovering well and possibly reduce the risk of future re-admission.
Online publication date: Wed, 31-Mar-2021
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