Breast Tissue
Donated on 5/9/2010
Dataset with electrical impedance measurements of freshly excised tissue samples from the breast.
Dataset Characteristics
Multivariate
Subject Area
Life Science
Associated Tasks
Classification
Feature Type
Real
# Instances
106
# Features
-
Dataset Information
Additional Information
Impedance measurements were made at the frequencies: 15.625, 31.25, 62.5, 125, 250, 500, 1000 KHz Impedance measurements of freshly excised breast tissue were made at the follwoing frequencies: 15.625, 31.25, 62.5, 125, 250, 500, 1000 KHz. These measurements plotted in the (real, -imaginary) plane constitute the impedance spectrum from where the breast tissue features are computed. The dataset can be used for predicting the classification of either the original 6 classes or of 4 classes by merging together the fibro-adenoma, mastopathy and glandular classes whose discrimination is not important (they cannot be accurately discriminated anyway).
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Demographic | Description | Units | Missing Values |
|---|---|---|---|---|---|---|
| no | ||||||
| no | ||||||
| no | ||||||
| no | ||||||
| no | ||||||
| no | ||||||
| no | ||||||
| no | ||||||
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| no |
0 to 10 of 10
Additional Variable Information
I0 Impedivity (ohm) at zero frequency PA500 phase angle at 500 KHz HFS high-frequency slope of phase angle DA impedance distance between spectral ends AREA area under spectrum A/DA area normalized by DA MAX IP maximum of the spectrum DR distance between I0 and real part of the maximum frequency point P length of the spectral curve Class car(carcinoma), fad (fibro-adenoma), mas (mastopathy), gla (glandular), con (connective), adi (adipose). The
S,JP and Jossinet,J. (2010). Breast Tissue. UCI Machine Learning Repository. https://doi.org/10.24432/C5P31H.
@misc{misc_breast_tissue_192,
author = {S,JP and Jossinet,J},
title = {{Breast Tissue}},
year = {2010},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5P31H}
}pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset breast_tissue = fetch_ucirepo(id=192) # data (as pandas dataframes) X = breast_tissue.data.features y = breast_tissue.data.targets # metadata print(breast_tissue.metadata) # variable information print(breast_tissue.variables)
Creators
JP S
J Jossinet
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.