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Hospital readmissions pose a significant burden on healthcare systems, especially for patients with type 2 diabetes mellitus (T2DM) and cardiovascular diseases. Early readmission risk prediction is crucial for improving patient outcomes and reducing costs. In this study, we develop a predictive model based on accessible clinical features to estimate the risk of future hospitalizations. Using data from 260 patients at the Republican Specialized Scientific and Practical Medical Center for Cardiology in Uzbekistan, we trained a Generalized Linear Model that achieved a ROC AUC of 0.898 on the test set.

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Predicting Risk of Future Hospitalizations in Patients with Type 2 Diabetes Mellitus and Cardiovascular Diseases Using Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Alisher Ikramov,
  • Shakhnoza Mukhtarova,
  • Raisa Trigulova,
  • Dilnoza Alimova,
  • Dilafruz Akhmedova

摘要

Hospital readmissions pose a significant burden on healthcare systems, especially for patients with type 2 diabetes mellitus (T2DM) and cardiovascular diseases. Early readmission risk prediction is crucial for improving patient outcomes and reducing costs. In this study, we develop a predictive model based on accessible clinical features to estimate the risk of future hospitalizations. Using data from 260 patients at the Republican Specialized Scientific and Practical Medical Center for Cardiology in Uzbekistan, we trained a Generalized Linear Model that achieved a ROC AUC of 0.898 on the test set.