Patient readmission to hospital after prior visits for the same disease results in an additional burden on healthcare providers, the health system, and patients. Machine learning (ML) models, if built and trained properly, can help understand reasons for readmission, and predict readmission accurately. ML could allow providers to create better treatment plans and care, which would translate to a reduction of both cost and mental stress for patients. However, ML is a complex technique that has been limiting organizations that don’t have the resources to recruit a team of data engineers and scientists to build ML workloads. In this post, we show you how to build an ML model based on the XGBoost algorithm to predict diabetic patient readmission easily and quickly with a graphical interface from Amazon SageMaker Data Wrangler.
Data Wrangler is an Amazon SageMaker Studio feature designed to allow you to explore and transform tabular data for

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