Prediction of Diabetic Patient Readmission Using Machine Learning

  • Juan Camilo RamírezEmail author
  • David Herrera
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1096)


Hospital readmissions pose additional costs and discomfort for the patient and their occurrences are indicative of deficient health service quality, hence efforts are generally made by medical professionals in order to prevent them. These endeavors are especially critical in the case of chronic conditions, such as diabetes. Recent developments in machine learning have been successful at predicting readmissions from the medical history of the diabetic patient. However, these approaches rely on a large number of clinical variables thereby requiring deep learning techniques. This article presents the application of simpler machine learning models achieving superior prediction performance while making computations more tractable.


Diabetes Hospital readmission Neural network Random forest Logistic regression Support vector machines 


  1. 1.
    Axon, R.N., Williams, M.V.: Hospital readmission as an accountability measure. JAMA 305(5), 504–505 (2011)CrossRefGoogle Scholar
  2. 2.
    Bhuvan, M.S., Kumar, A., Zafar, A., Kishore, V.: Identifying diabetic patients with high risk of readmission. arXiv preprint arXiv:1602.04257 (2016)
  3. 3.
    Boulding, W., Glickman, S.W., Manary, M.P., Schulman, K.A., Staelin, R.: Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. Am. J. Managed Care 17(1), 41–48 (2011)Google Scholar
  4. 4.
    Breen, T.J., Patel, H., Purga, S., Fein, S.A., Philbin, E.F., Torosoff, M.: Effects of index admission length of stay on readmission interval in patients with heart failure. Circ.: Cardiovasc. Qual. Outcomes 11(suppl\(\_\)1), A275–A275 (2018)Google Scholar
  5. 5.
    Brisimi, T.S., Xu, T., Wang, T., Dai, W., Adams, W.G., Paschalidis, I.C.: Predicting chronic disease hospitalizations from electronic health records: an interpretable classification approach. Proc. IEEE 106(4), 690–707 (2018)CrossRefGoogle Scholar
  6. 6.
    Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N.: Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1721–1730. ACM (2015)Google Scholar
  7. 7.
    Chou, E., Nguyen, T., Beal, J., Haque, A., Fei-Fei, L.: A fully private pipeline for deep learning on electronic health records. arXiv preprint arXiv:1811.09951 (2018)
  8. 8.
    Choudhry, S.A., Li, J., Davis, D., Erdmann, C., Sikka, R., Sutariya, B.: A public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model. Online J. Publ. Health Inform. 5(2), 219 (2013)CrossRefGoogle Scholar
  9. 9.
    Choudhury, A., Greene, D., Christopher, M.: Evaluating patient readmission risk: a predictive analytics approach. arXiv preprint arXiv:1812.11028 (2018)
  10. 10.
    CPT: Current Procedural Terminology (CPT) American Medical Association (2014).
  11. 11.
    Duggal, R., Shukla, S., Chandra, S., Shukla, B., Khatri, S.K.: Predictive risk modelling for early hospital readmission of patients with diabetes in India. Int. J. Diab. Dev. Countries 36(4), 519–528 (2016)CrossRefGoogle Scholar
  12. 12.
    Frizzell, J.D., et al.: Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches. JAMA Cardiol. 2(2), 204–209 (2017)CrossRefGoogle Scholar
  13. 13.
    Futoma, J., Morris, J., Lucas, J.: A comparison of models for predicting early hospital readmissions. J. Biomed. Inform. 56, 229–238 (2015)CrossRefGoogle Scholar
  14. 14.
    Hammoudeh, A., Al-Naymat, G., Ghannam, I., Obied, N.: Predicting hospital readmission among diabetics using deep learning. Procedia Comput. Sci. 141, 484–489 (2018)CrossRefGoogle Scholar
  15. 15.
    He, D., Mathews, S.C., Kalloo, A.N., Hutfless, S.: Mining high-dimensional administrative claims data to predict early hospital readmissions. J. Am. Med. Inform. Assoc. 21(2), 272–279 (2014)CrossRefGoogle Scholar
  16. 16.
    Hosseinzadeh, A., Izadi, M., Verma, A., Precup, D., Buckeridge, D.: Assessing the predictability of hospital readmission using machine learning. In: Twenty-Fifth IAAI Conference (2013)Google Scholar
  17. 17.
    Kansagara, D., et al.: Risk prediction models for hospital readmission: a systematic review. JAMA 306(15), 1688–1698 (2011)CrossRefGoogle Scholar
  18. 18.
    Kaschel, H., Rocco, V., Reinao, G.: An open algorithm for systematic evaluation of readmission predictors on diabetic patients from data warehouses. In: 2018 IEEE International Conference on Automation/XXIII Congress of the Chilean Association of Automatic Control, pp. 1–6. IEEE (2018)Google Scholar
  19. 19.
    Kassin, M.T., et al.: Risk factors for 30-day hospital readmission among general surgery patients. J. Am. Coll. Surg. 215(3), 322–330 (2012)CrossRefGoogle Scholar
  20. 20.
    King, C., et al.: Identifying risk factors for 30-day readmission events among American Indian patients with diabetes in the four corners region of the southwest from 2009 to 2016. PLoS ONE 13(8), e0195476 (2018)CrossRefGoogle Scholar
  21. 21.
    Kripalani, S., Theobald, C.N., Anctil, B., Vasilevskis, E.E.: Reducing hospital readmission rates: current strategies and future directions. Ann. Rev. Med. 65, 471–485 (2014)CrossRefGoogle Scholar
  22. 22.
    Krumholz, H.M., et al.: Readmission after hospitalization for congestive heart failure among medicare beneficiaries. Arch. Intern. Med. 157(1), 99–104 (1997)CrossRefGoogle Scholar
  23. 23.
    Merkow, R.P., et al.: Underlying reasons associated with hospital readmission following surgery in the United States. JAMA 313(5), 483–495 (2015)CrossRefGoogle Scholar
  24. 24.
    Mingle, D.: Predicting diabetic readmission rates: moving beyond Hba1c. Curr. Trends Biomed. Eng. Biosci. 7(3), 555707 (2017)CrossRefGoogle Scholar
  25. 25.
    Moore, B.J., White, S., Washington, R., Coenen, N., Elixhauser, A.: Identifying increased risk of readmission and in-hospital mortality using hospital administrative data. Med. Care 55(7), 698–705 (2017)CrossRefGoogle Scholar
  26. 26.
    Mortazavi, B.J., et al.: Analysis of machine learning techniques for heart failure readmissions. Circ. Cardiovasc. Qual. Outcomes 9(6), 629–640 (2016)Google Scholar
  27. 27.
    Rubin, D.J.: Hospital readmission of patients with diabetes. Curr. Diab. Rep. 15(4), 17 (2015)CrossRefGoogle Scholar
  28. 28.
    Soysal, Ö.M.: Association rule mining with mostly associated sequential patterns. Expert Syst. Appl. 42(5), 2582–2592 (2015)CrossRefGoogle Scholar
  29. 29.
    Strack, B., et al.: Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records. BioMed Res. Int. 2014 (2014) CrossRefGoogle Scholar
  30. 30.
    Tutun, S., Khanmohammadi, S., He, L., Chou, C.A.: A meta-heuristic LASSO model for diabetic readmission prediction. In: Proceedings of the 2016 Industrial and Systems Engineering Research Conference (ISERC) (2016)Google Scholar
  31. 31.
    WHO: International classification of diseases, 9th revision (ICD-9) - World Health Organization (1999).
  32. 32.
    Yu, S., Farooq, F., Van Esbroeck, A., Fung, G., Anand, V., Krishnapuram, B.: Predicting readmission risk with institution-specific prediction models. Artif. Intell. Med. 65(2), 89–96 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universidad Antonio NariñoBogotáColombia

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