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.
|Original language||English (US)|
|Number of pages||16|
|Journal||International Journal of Computational Biology and Drug Design|
|State||Published - 2020|
All Science Journal Classification (ASJC) codes
- Drug Discovery
- Computer Science Applications