Z-Alizadeh Sani
Donated on 11/16/2017
It was collected for CAD diagnosis.
Dataset Characteristics
Tabular
Subject Area
Health and Medicine
Associated Tasks
Classification
Feature Type
Integer, Real
# Instances
303
# Features
-
Dataset Information
Additional Information
Each patient could be in two possible categories CAD or Normal. A patient is categorized as CAD, if his/her diameter narrowing is greater than or equal to 50%, and otherwise as Normal .
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no |
0 to 10 of 56
Additional Variable Information
The Z-Alizadeh Sani dataset contains the records of 303 patients, each of which have 54 features.The features are arranged in four groups: demographic, symptom and examination, ECG, and laboratory and echo features.
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset z_alizadeh_sani = fetch_ucirepo(id=412) # data (as pandas dataframes) X = z_alizadeh_sani.data.features y = z_alizadeh_sani.data.targets # metadata print(z_alizadeh_sani.metadata) # variable information print(z_alizadeh_sani.variables)
Alizadehsani,Roohallah, Roshanzamir,Mohamad, and Sani,Zahra. (2017). Z-Alizadeh Sani. UCI Machine Learning Repository. https://doi.org/10.24432/C5Q31T.
@misc{misc_z-alizadeh_sani_412,
author = {Alizadehsani,Roohallah, Roshanzamir,Mohamad, and Sani,Zahra},
title = {{Z-Alizadeh Sani}},
year = {2017},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5Q31T}
}Creators
Roohallah Alizadehsani
Mohamad Roshanzamir
Zahra Sani
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.