Predicting re-admission to hospital for diabetes treatment: A machine learning solution

Satish M. Srinivasan, Yok Fong Paat, Philmore Halls, Ruth Kalule, Thomas E. Harvey

Research output: Contribution to journalArticlepeer-review


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 languageEnglish (US)
Pages (from-to)539-554
Number of pages16
JournalInternational Journal of Computational Biology and Drug Design
Issue number5-6
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • Drug Discovery
  • Computer Science Applications

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