Our project, inspired by the need to improve diabetes patient care, sought to uncover patterns in readmissions. We focused on distinguishing between diabetes patients readmitted within 30 days and those readmitted after, aiming to answer whether measurable differences exist. Leveraging data-driven approaches, we conducted statistical analysis and machine learning, overcoming challenges in data quality and interpretation. Our work aimed to provide valuable insights that could enhance diabetes patient care and outcomes.
Back to the Data: Exploring Diabetes
Do measurable patterns exist between diabetes patients who are readmitted before or after 30 days from their previous visit?
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