{"id":1217,"date":"2021-11-18T08:32:49","date_gmt":"2021-11-18T08:32:49","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2021\/11\/18\/accelerate-data-preparation-using-amazon-sagemaker-data-wrangler-for-diabetic-patient-readmission-prediction\/"},"modified":"2021-11-18T08:32:49","modified_gmt":"2021-11-18T08:32:49","slug":"accelerate-data-preparation-using-amazon-sagemaker-data-wrangler-for-diabetic-patient-readmission-prediction","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2021\/11\/18\/accelerate-data-preparation-using-amazon-sagemaker-data-wrangler-for-diabetic-patient-readmission-prediction\/","title":{"rendered":"Accelerate data preparation using Amazon SageMaker Data Wrangler for diabetic patient readmission prediction"},"content":{"rendered":"
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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\u2019t 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<\/a>.<\/p>\n

Data Wrangler is an Amazon SageMaker Studio<\/a> feature designed to allow you to explore and transform tabular data for ML use cases without coding. Data Wrangler is the fastest and easiest way to prepare data for ML. It gives you the ability to use a visual interface to access data and perform exploratory data analysis (EDA) and feature engineering. It also seamlessly operationalizes your data preparation steps by allowing you to export your data flow into Amazon SageMaker Pipelines<\/a>, a Data Wrangler job, Python file, or Amazon SageMaker Feature Store<\/a>.<\/p>\n

Data Wrangler comes with over 300 built-in transforms and custom transformations using either Python, PySpark, or SparkSQL runtime. It also comes with built-in data analysis capabilities for charts (such as scatter plot or histogram) and time-saving model analysis capabilities such as feature importance, target leakage, and model explainability.<\/p>\n

In this post, we explore the key capabilities of Data Wrangler using the UCI diabetic patient readmission dataset<\/a>. We showcase how you can build ML data transformation steps without writing sophisticated coding, and how to create a model training, feature store, or ML pipeline with reproducibility for a diabetic patient readmission prediction use case.<\/p>\n

We also have published a related GitHub project repo<\/a> that includes the end-to-end ML workflow steps and relevant assets, including Jupyter notebooks.<\/p>\n

We walk you through the following high-level steps:<\/p>\n