Sold by: Virtusa Corporation - vLifeÂ
Deployed on AWS
Machine Learning model to predict the chance of a subject prone to Heart Disease based on the risk factors and clinical measures.
Overview
Heart disease describes a range of conditions that affect your heart. It generally refers to conditions that involve narrowed or blocked blood vessels that can lead to a heart attack, chest pain or stroke. Heart conditions, such as those that affect your heart's muscle, valves or rhythm, also are considered forms of heart disease. In this POC the model identifies patients at risk of heart disease based on the subject’s risk factors and clinical measures. The data on which the model is trained is a variant of US simulated population and its large size makes the model very robust and bias free.
Highlights
- Easy-to-use model quickly determines whether a patients's observations can be used to predict the likeliness of disease.
- Provides the most realistic scenario as it is trained on a large dataset capturing all possible patterns.
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $0.00 |
ml.m4.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $0.00 |
ml.m4.4xlarge Inference (Real-Time) | Model inference on the ml.m4.4xlarge instance type, real-time mode | $0.00 |
ml.m5.4xlarge Inference (Real-Time) | Model inference on the ml.m5.4xlarge instance type, real-time mode | $0.00 |
ml.m4.16xlarge Inference (Real-Time) | Model inference on the ml.m4.16xlarge instance type, real-time mode | $0.00 |
ml.m5.2xlarge Inference (Real-Time) | Model inference on the ml.m5.2xlarge instance type, real-time mode | $0.00 |
ml.p3.16xlarge Inference (Real-Time) | Model inference on the ml.p3.16xlarge instance type, real-time mode | $0.00 |
ml.m4.2xlarge Inference (Real-Time) | Model inference on the ml.m4.2xlarge instance type, real-time mode | $0.00 |
ml.c5.2xlarge Inference (Real-Time) | Model inference on the ml.c5.2xlarge instance type, real-time mode | $0.00 |
ml.p3.2xlarge Inference (Real-Time) | Model inference on the ml.p3.2xlarge instance type, real-time mode | $0.00 |
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This product is offered for free. If there are any questions, please contact us for further clarifications.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
More info @ virtusa.com/vlife
Additional details
Inputs
- Summary
Heart Disease Patient Identifier. See Notebook for additional information.
- Input MIME type
- text/csv
See Input Summary
See Input Summary
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Vendor resources
Support
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Propensity model that determines the probability that a consumer has Heart Disease.
This data set is made up of highly realistic, but synthetic patient data that can be used for testing purposes without risk of disclosing PHI (protected health information).
We offer bespoke synthetic data to meet your needs. Contact us for more information.
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