Heart-Disease-patients
OpenML dataset with id 43562
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22102387/Heart-Disease-patients.arff
Upload date: 23 March 2022
Dataset Characteristics
Number of features: 12 (numeric: 12, symbolic: 0 and in total binary: 0 )
Number of instances: 303
Number of instances with missing values: 0
Number of missing values: 0
Context
There are many industries where understanding how things group together is beneficial. For example, retailers want to understand the similarities among their customers to direct advertisement campaigns, and botanists classify plants based on their shared similar characteristics. One way to group objects is to use clustering algorithms. We are going to explore the usefulness of unsupervised clustering algorithms to help doctors understand which treatments might work with their patients.
Content
We are going to cluster anonymized data of patients who have been diagnosed with heart disease. Patients with similar characteristics might respond to the same treatments, and doctors could benefit from learning about the treatment outcomes of patients like those they are treating. The data we are analyzing comes from the V.A. Medical Center in Long Beach, CA. To download the data, visit here.
Before running any analysis, it is essential to get an idea of what the data look like. The clustering algorithms we will use require numeric datawe'll check that all the data are numeric.
Acknowledgements
DataCamp
This page was built for dataset: Heart-Disease-patients
