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

2 Scopus citations


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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