Breast Cancer Classification Model is applied on a different dataset

Last update: Feb 4, 2022

Overview

Breast-Cancer-Classification-model-2.0

Same model as in https://github.com/PavanNettam/Breast-Cancer-Classification-Model is applied on a different dataset
Data Set Information:

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at [Web Link]

Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.

The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/
Attribute Information:

ID number
Diagnosis (M = malignant, B = benign) 3-32)

Ten real-valued features are computed for each cell nucleus:

a) radius (mean of distances from center to points on the perimeter) b) texture (standard deviation of gray-scale values) c) perimeter d) area e) smoothness (local variation in radius lengths) f) compactness (perimeter^2 / area - 1.0) g) concavity (severity of concave portions of the contour) h) concave points (number of concave portions of the contour) i) symmetry j) fractal dimension ("coastline approximation" - 1)
Classfication results:

The dataset is divided into two parts, Trainings set(80% of the dataset) Testing set(20% of the dataset)

Logistic regression model is applied on the training dataset and when tested on the testing dataset the following results were obtained:

Confusion matrix:

Accuracy Score = 0.956140350877193
K-Fold cross validation shows the following accuracy for the model:

Accuracy = 98.18% Standard Deviation = 3.64%

Dataset is taken from the following webpage: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

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Breast Cancer Classification Model is applied on a different dataset

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