Mammographic Mass Classification Log Regression Approach
Introduction
Discrimination of benign and malignant mammographic masses based on some attributes, Building a ML model using scikit-learn's class LogisticRegression
Dataset
6 Attributes in total (1 goal field, 1 non-predictive, 4 predictive attributes)
- BI-RADS assessment: 1 to 5 (ordinal)
- Age: patient's age in years (integer)
- Shape: mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal)
- Margin: mass margin: circumscribed=1 microlobulated=2 obscured=3 ill-defined=4 spiculated=5 (nominal)
- Density: mass density high=1 iso=2 low=3 fat-containing=4 (ordinal)
- Severity: benign=0 or malignant=1 (binominal, goal field!)*
Source
"Mammographic masses" public dataset from the UCI repository has been used. (source: https://archive.ics.uci.edu/ml/datasets/Mammographic+Mass)
Matthias Elter
Fraunhofer Institute for Integrated Circuits (IIS)
Image Processing and Medical Engineering Department (BMT)
Am Wolfsmantel 33
91058 Erlangen, Germany
matthias.elter@iis.fraunhofer.de
(49) 9131-7767327
Prof. Dr. Rüdiger Schulz-Wendtland
Institute of Radiology, Gynaecological Radiology, University Erlangen-Nuremberg
Universitätsstraße 21-23
91054 Erlangen, Germany
Results
Metrics shows some good results, as the following confusion matrix suggest
There are some others metrics supporting remarkable accuracy
Improvements
Seeking improve model accuracy, some tunning parameters or alternative techniques for supervised classification might helped in order to achieve it