Breast cancer

Files	Size	Format	Created	Updated	License	Source
3	101kB	arff csv zip	1 year ago	11 months ago	Open Data Commons Public Domain Dedication and License

File	Description	Size	Download
breast-cancer_arff		29kB	arff (29kB)
breast-cancer		19kB	csv (19kB) , json (60kB)
breast-cancer_zip	Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json.	14kB	zip (14kB)

Field Name	Order	Type (Format)
age	1	string (default)
menopause	2	string (default)
tumor-size	3	string (default)
inv-nodes	4	string (default)
node-caps	5	string (default)
deg-malig	6	number (default)
breast	7	string (default)
breast-quad	8	string (default)
irradiat	9	string (default)
Class	10	string (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/machine-learning/breast-cancer
data info machine-learning/breast-cancer
tree machine-learning/breast-cancer

# Get a list of dataset's resources
curl -L -s https://datahub.io/machine-learning/breast-cancer/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/machine-learning/breast-cancer/r/0.arff

curl -L https://datahub.io/machine-learning/breast-cancer/r/1.csv

curl -L https://datahub.io/machine-learning/breast-cancer/r/2.zip

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/machine-learning/breast-cancer/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))
    print(data)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/machine-learning/breast-cancer/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('https://datahub.io/machine-learning/breast-cancer/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        print(resource.read())

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'https://datahub.io/machine-learning/breast-cancer/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()

Read me

The resources for this dataset can be found at https://www.openml.org/d/13

Author:
Source: Unknown -
Please cite:

Citation Request: This breast cancer domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. Soklic for providing the data. Please include this citation if you plan to use this database.

Title: Breast cancer data (Michalski has used this)
Sources: – Matjaz Zwitter & Milan Soklic (physicians) Institute of Oncology University Medical Center Ljubljana, Yugoslavia – Donors: Ming Tan and Jeff Schlimmer ([email protected]) – Date: 11 July 1988
Past Usage: (Several: here are some) – Michalski,R.S., Mozetic,I., Hong,J., & Lavrac,N. (1986). The Multi-Purpose Incremental Learning System AQ15 and its Testing Application to Three Medical Domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, 1041-1045, Philadelphia, PA: Morgan Kaufmann. – accuracy range: 66%-72% – Clark,P. & Niblett,T. (1987). Induction in Noisy Domains. In Progress in Machine Learning (from the Proceedings of the 2nd European Working Session on Learning), 11-30, Bled, Yugoslavia: Sigma Press. – 8 test results given: 65%-72% accuracy range – Tan, M., & Eshelman, L. (1988). Using weighted networks to represent classification knowledge in noisy domains. Proceedings of the Fifth International Conference on Machine Learning, 121-134, Ann Arbor, MI. – 4 systems tested: accuracy range was 68%-73.5% – Cestnik,G., Konenenko,I, & Bratko,I. (1987). Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users. In I.Bratko & N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press. – Assistant-86: 78% accuracy
Relevant Information: This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. (See also lymphography and primary-tumor.)

This data set includes 201 instances of one class and 85 instances of another class. The instances are described by 9 attributes, some of which are linear and some are nominal.
Number of Instances: 286
Number of Attributes: 9 + the class attribute
Attribute Information:
1. Class: no-recurrence-events, recurrence-events
2. age: 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99.
3. menopause: lt40, ge40, premeno.
4. tumor-size: 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59.
5. inv-nodes: 0-2, 3-5, 6-8, 9-11, 12-14, 15-17, 18-20, 21-23, 24-26, 27-29, 30-32, 33-35, 36-39.
6. node-caps: yes, no.
7. deg-malig: 1, 2, 3.
8. breast: left, right.
9. breast-quad: left-up, left-low, right-up, right-low, central.
irradiat: yes, no.
Missing Attribute Values: (denoted by “?”) Attribute #: Number of instances with missing values: 6. 8 9. 1.
Class Distribution:
1. no-recurrence-events: 201 instances
2. recurrence-events: 85 instances

Num Instances: 286 Num Attributes: 10 Num Continuous: 0 (Int 0 / Real 0) Num Discrete: 10 Missing values: 9 / 0.3%

 name                      type enum ints real     missing    distinct  (1)

1 ‘age’ Enum 100% 0% 0% 0 / 0% 6 / 2% 0% 2 ‘menopause’ Enum 100% 0% 0% 0 / 0% 3 / 1% 0% 3 ‘tumor-size’ Enum 100% 0% 0% 0 / 0% 11 / 4% 0% 4 ‘inv-nodes’ Enum 100% 0% 0% 0 / 0% 7 / 2% 0% 5 ‘node-caps’ Enum 97% 0% 0% 8 / 3% 2 / 1% 0% 6 ‘deg-malig’ Enum 100% 0% 0% 0 / 0% 3 / 1% 0% 7 ‘breast’ Enum 100% 0% 0% 0 / 0% 2 / 1% 0% 8 ‘breast-quad’ Enum 100% 0% 0% 1 / 0% 5 / 2% 0% 9 ‘irradiat’ Enum 100% 0% 0% 0 / 0% 2 / 1% 0% 10 ‘Class’ Enum 100% 0% 0% 0 / 0% 2 / 1% 0%

Some of your data has validation errors:

Breast cancer

Data Files

breast-cancer_arff

breast-cancer

Field information

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