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by Justin McCoy, David Carew | Updated May 18, 2018 - Published August 22, 2017
AnalyticsArtificial intelligenceData scienceDeep learningMachine learningNode.jsObject StoragePlatform as a ServicePythonCloud
Machine learning is branching out across numerous fields, one of the most interesting fields is health care. This code pattern uses a Jupyter Notebook on IBM Watson Studio to build a predictive model that demonstrates a potential healthcare use case. This predictive model is deployed into production on Watson’s Machine Learning Service and invoked by a custom Node.js app running on a Cloud Foundry Runtime in IBM Cloud.
You’re a busy developer or data scientist and want the fastest path delivering data insights to users, but this requires deep expertise in many technology domains. This end-to-end example walks you through the numerous technologies used to:
Along the way, you’ll learn about IBM’s Watson Machine Learning Service for hosting your trained model on IBM’s Cloud, and IBM Watson Studio, a Cloud-based IDE for data science teams; tools that bring together many open-source technologies built for data science and machine learning.
In this code pattern, you will use a Jupyter Notebook on IBM Watson Studio to build a predictive model that demonstrates a potential healthcare use case. Although this is for demonstrative purposes only, you’ll see how to use Watson Machine Learning on a data set comprised of health care metrics to create a predictive model for risk of heart failure. After creating this model, inputs that are entered can be scored to form a prediction for an individual case. Note that this application is used for demonstrative and illustrative purposes only and does not constitute an offering that has gone through regulatory review.
After completing this code pattern, you will understand how to:
Find the detailed steps for this pattern in the README.md. The steps will show you how to:
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