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Hepatitis

machine-learning

Files Size Format Created Updated License Source
2 47kB csv zip 1 year ago 1 year ago OpenML - hepatitis
This dataset contains occurrences of hepatitis in people. Data This dataset was found on OpenML - hepatitis Donor: G.Gong (Carnegie-Mellon University) via Bojan Cestnik Jozef Stefan Institute Jamova 39 61000 Ljubljana Yugoslavia Attribute information bilirubin: 0.3 - 4.8 alk_phosphate: 33 - read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
hepatitis 15kB csv (15kB) , json (55kB)
hepatitis_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 11kB zip (11kB)

hepatitis  

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This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
age 1 integer (default)
sex 2 string (default)
steroid 3 boolean (default)
antivirals 4 boolean (default)
fatigue 5 boolean (default)
malaise 6 boolean (default)
anorexia 7 boolean (default)
liver_big 8 boolean (default)
liver_firm 9 boolean (default)
spleen_palpable 10 boolean (default)
spiders 11 boolean (default)
ascites 12 boolean (default)
varices 13 boolean (default)
bilirubin 14 number (default)
alk_phosphate 15 integer (default)
sgot 16 integer (default)
albumin 17 number (default)
protime 18 integer (default)
histology 19 boolean (default)
class 20 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/hepatitis
data info machine-learning/hepatitis
tree machine-learning/hepatitis
# Get a list of dataset's resources
curl -L -s https://datahub.io/machine-learning/hepatitis/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/machine-learning/hepatitis/r/0.csv

curl -L https://datahub.io/machine-learning/hepatitis/r/1.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/hepatitis/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/hepatitis/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/hepatitis/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/hepatitis/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

This dataset contains occurrences of hepatitis in people.

Data

This dataset was found on OpenML - hepatitis

Donor: G.Gong (Carnegie-Mellon University) via Bojan Cestnik Jozef Stefan Institute Jamova 39 61000 Ljubljana Yugoslavia

Attribute information

  • bilirubin: 0.3 - 4.8
  • alk_phosphate: 33 - 250
  • sgot: 13 - 500
  • albumin: 2.1 - 6.0
  • protime: 10 - 100
  • others: true - false

Data is located in directory data

data/hepatitis.csv

Preparation

This script should be run using Python 3.

Scripts are in directory scripts

scripts/main.py

License

Licensed under the Public Domain Dedication and Licence (assuming either no rights or public domain licence in source data).

Datapackage.json

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