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Heart Disease Risk Prediction using scikit-learn* (sklearn) and XGBoost: Developer Spotlight

Ramya_Ravi
Employee
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Heart Disease is a broad term that refers to any problem affecting the heart. These diseases affect the cardiovascular system. There are several types of heart diseases, and they have an effect on the heart and blood vessels in different ways. Heart disease can be dangerous and deadly, but it is also preventable in most people. Thus, early, and automatic detection of heart diseases can save many human lives.

Arnab Das in his blog proposed a solution to Heart Disease Risk Prediction using Intel® Extension for Scikit-learn*, Intel® Developer Cloud and Intel® Optimization for XGBoost*.

Article Highlights:

The blog explains the various steps involved in the project:

  1. Installation of required libraries.
  2. Data Collection & Preparation using Intel® Distribution of Modin*
  3. Model Training
  4. Model Testing

Read and learn more about the project at Heart Disease Risk Prediction using Intel® AI Analytics Toolkit (AI Kit).

Learn more about Intel Extension for Scikit-learn and Intel Optimization for XGBoost!!!

Intel Extension for Scikit-learn: This Intel Extension seamlessly speeds up your scikit-learn applications for Intel architectures across single and multi-node configurations. Also, this extension package dynamically patches scikit-learn estimators while improving performance for your machine learning algorithms. By using Scikit-learn with this extension, the performance for training and inference can be improved up to 100x with the equivalent mathematical accuracy.

Intel Optimization for XGBoost: XGBoost with Intel optimizations will automatically accelerate XGBoost training and inference performance on Intel CPUs. Additionally, these optimizations will help to further accelerate inference on Intel CPUs with advanced features not yet available in XGBoost by importing models into daal4py.

Download Intel Extension for Scikit-learn, Intel Optimization for XGBoost and Intel Distribution of Modin as part of the Intel® AI Analytics Toolkit (AI Kit).

What’s Next?

We encourage you to check out and incorporate Intel’s other AI/ML Framework optimizations and end-to-end portfolio of tools into your AI workflow and learn about the unified, open, standards-based oneAPI programming model that forms the foundation of Intel’s AI Software Portfolio to help you prepare, build, deploy, and scale your AI solutions.

For more details about the new 4th Gen Intel® Xeon® Scalable processors, visit Intel's AI Solution Platform portal where you can learn how Intel is empowering developers to run end-to-end AI pipelines on these powerful CPUs.

About the Author:

Arnab Das is currently working as a Full Stack Engineer at EdgeVerve in Bengaluru, India. His major interests are artificial intelligence, machine learning and blockchain technology.

 

About the Author
Product Marketing Engineer bringing cutting edge AI/ML solutions and tools from Intel to developers.