Mammographic-Mass-Data-Set

OpenML dataset with id 45557

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Full work available at URL: https://api.openml.org/data/v1/download/22116525/Mammographic-Mass-Data-Set.arff

Upload date: 5 June 2023

Dataset Characteristics

Number of classes: 2
Number of features: 5 (numeric: 2, symbolic: 3 and in total binary: 1 )
Number of instances: 961
Number of instances with missing values: 130
Number of missing values: 160

Description

Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis (CAD) systems have been proposed in the last years.These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead.

This data set can be used to predict the severity (benign or malignant) of a mammographic mass lesion from BI-RADS attributes and the patient's age. It contains a BI-RADS assessment, the patient's age and three BI-RADS attributes together with the ground truth (the severity field) for 516 benign and 445 malignant masses that have been identified on full field digital mammograms collected at the Institute of Radiology of the University Erlangen-Nuremberg between 2003 and 2006.

Each instance has an associated BI-RADS assessment ranging from 1 (definitely benign) to 5 (highly suggestive of malignancy) assigned in a double-review process by physicians. Assuming that all cases with BI-RADS assessments greater or equal a given value (varying from 1 to 5), are malignant and the other cases benign, sensitivities and associated specificities can be calculated. These can be an indication of how well a CAD system performs compared to the radiologists.

Class Distribution: benign: 516; malignant: 445

Attributes

6 Attributes in total (1 goal field, 1 non-predictive, 4 predictive attributes)

1. BI-RADS assessment: 1 to 5 (ordinal, non-predictive!) 2. Age: patient's age in years (integer) 3. Shape: mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal) 4. Margin: mass margin: circumscribed=1 microlobulated=2 obscured=3 ill-defined=4 spiculated=5 (nominal) 5. Density: mass density high=1 iso=2 low=3 fat-containing=4 (ordinal) 6. Severity: benign=0 or malignant=1 (binominal, goal field!)

Missing Attribute Values:

   - BI-RADS assessment:    2
   - Age:                   5
   - Shape:                31
   - Margin:               48
   - Density:              76
   - Severity:              0

Notes

Compared to v1 this dataset has the following difference:

It contains missing values. It appears that v1 has dropped all entries with missing values.
Variable types are coded more correctly. BI-RADS assessment and Density should be ordinal, but were coded as float because ordinal is not available on OpenML. They were not coded as int because liac-arff cannot serialize pd.NA yet.
The variable `BI-RADS assessment` is names `BI-RADS` because OpenML does not allow whitespace in attribute names.

This page was built for dataset: Mammographic-Mass-Data-Set