Breast Cancer Coimbra

Donated on 3/5/2018

Clinical features were observed or measured for 64 patients with breast cancer and 52 healthy controls.

Dataset Characteristics

Multivariate

Subject Area

Health and Medicine

Associated Tasks

Classification

Feature Type

Integer

# Instances

116

# Features

9

Dataset Information

Additional Information

There are 10 predictors, all quantitative, and a binary dependent variable, indicating the presence or absence of breast cancer. The predictors are anthropometric data and parameters which can be gathered in routine blood analysis. Prediction models based on these predictors, if accurate, can potentially be used as a biomarker of breast cancer.

Has Missing Values?

No

Introductory Paper

Using Resistin, glucose, age and BMI to predict the presence of breast cancer

By M. Patrício, J. Pereira, J. Crisóstomo, P. Matafome, M. Gomes, Raquel Seiça, F. Caramelo. 2018

Published in BMC Cancer

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
AgeFeatureIntegerAgeyearno
BMIFeatureContinuouskg/m2no
GlucoseFeatureIntegermg/dLno
InsulinFeatureContinuousµU/mLno
HOMAFeatureContinuousno
LeptinFeatureContinuousng/mLno
AdiponectinFeatureContinuousµU/mLno
ResistinFeatureContinuousng/mLno
MCP.1FeatureContinuouspg/dLno
ClassificationTargetInteger1=Healthy controls, 2=Patientsno

0 to 10 of 10

Additional Variable Information

Quantitative Attributes: Age (years) BMI (kg/m2) Glucose (mg/dL) Insulin (µU/mL) HOMA Leptin (ng/mL) Adiponectin (µg/mL) Resistin (ng/mL) MCP-1(pg/dL) Labels: 1=Healthy controls 2=Patients

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Creators

Miguel Patrcio

Jos Pereira

Joana Crisstomo

Paulo Matafome

Raquel Seia

Francisco Caramelo

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