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Clustering Heart Disease Patient Data

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Updated 06/2024
Experiment with clustering algorithms to help doctors inform treatment for heart disease patients.
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RData ManipulationData VisualizationMachine Learning45 minutes10 Tasks1,500 XP4,198

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Project Description

Clustering Heart Disease Patient Data

Doctors frequently study former cases to learn how to best treat their patients. A patient who has a similar health history or symptoms to a previous patient could benefit from undergoing the same treatment. This project investigates whether doctors might be able to group together patients to target treatments using common unsupervised learning techniques. In this project you will use k-means and hierarchical clustering algorithms. The dataset for this project contains characteristics of patients diagnosed with heart disease. It can be found [here](https://archive.ics.uci.edu/ml/datasets/heart+Disease).

Clustering Heart Disease Patient Data

Experiment with clustering algorithms to help doctors inform treatment for heart disease patients.
Start project for free
  • 1

    Targeting treatment for heart disease patients

  • 2

    Quantifying patient differences

  • 3

    Let's start grouping patients

  • 4

    Another round of k-means

  • 5

    Comparing patient clusters

  • 6

    Hierarchical clustering: another clustering approach

  • 7

    Hierarchical clustering round two

  • 8

    Comparing clustering results

  • 9

    Visualizing the cluster contents

  • 10

    Conclusion

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