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Clustering Heart Disease Patient Data

Experiment with clustering algorithms to help doctors inform treatment for heart disease patients.

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10 Tasks1,500 XP

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Project Description

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).

Project Tasks

  1. 1
    Targeting treatment for heart disease patients
  2. 2
    Quantifying patient differences
  3. 3
    Let's start grouping patients
  4. 4
    Another round of k-means
  5. 5
    Comparing patient clusters
  6. 6
    Hierarchical clustering: another clustering approach
  7. 7
    Hierarchical clustering round two
  8. 8
    Comparing clustering results
  9. 9
    Visualizing the cluster contents
  10. 10
    Conclusion

Technologies

R R

Topics

Data ManipulationData VisualizationMachine LearningImporting & Cleaning Data
Megan Robertson Headshot

Megan Robertson

Data Scientist

Megan Robertson is a data scientist with a background in machine learning and Bayesian statistics. She earned a Master's of Statistical Science from Duke University and has multiple years of experience teaching math and statistics. She is interested in sports analytics and interned with the Charlotte Hornets while in graduate school.
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