Data
Heart-Disease-patients

Heart-Disease-patients

active ARFF GPL 2 Visibility: public Uploaded 23-03-2022 by Onur Yildirim
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • Computer Systems Machine Learning
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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

12 features

idnumeric303 unique values
0 missing
agenumeric41 unique values
0 missing
sexnumeric2 unique values
0 missing
cpnumeric4 unique values
0 missing
trestbpsnumeric50 unique values
0 missing
cholnumeric152 unique values
0 missing
fbsnumeric2 unique values
0 missing
restecgnumeric3 unique values
0 missing
thalachnumeric91 unique values
0 missing
exangnumeric2 unique values
0 missing
oldpeaknumeric40 unique values
0 missing
slopenumeric3 unique values
0 missing

19 properties

303
Number of instances (rows) of the dataset.
12
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
12
Number of numeric attributes.
0
Number of nominal attributes.
0.04
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

0 tasks

Define a new task