Abstract
Breast cancer is one of the rapid spreading diseases resulting in the death of younger age group of women. Unfortunately, as the detection of cancer is at later stage, the lifetime of the patient is decreased. If the detection is at early stage, then their lifetime could have been improved. Hence, the proposal aims at predicting the presence of breast cancer at early stage through deep learning. To identify suitable model for deep learning, initially machine learning algorithm with Logistic Regression, K Nearest Neighbors, Support Vector Machine (linear), Support Vector Machine, Gaussian, Decision Tree and Random Forest along with ensemble learning algorithms such as Bagged Trees, Subspace discriminant and RUSBoosted Trees are implemented with 30 attributes. Comparison of performance metrices indicates that random forest performs better. Then, feature selection of 14 attributes is attained through heat map. With minimal features, the above set of algorithms is implemented and their corresponding performance indices such as accuracy, misclassification cost, prediction speed, training time, predicted class, true class, positive predict value, sensitivity, specificity, precision, F1 score, Area Under the Curve and Receiver Operating Characteristic Curve are obtained. In this, random forest performs better and in addition, the performance of 14 attributes is almost exhibiting closer performance as that of 30. However, feature selection is mandate and can be eliminated if the algorithm is implemented through deep learning model. The model consists of many hidden layers which performs binary classification on the given dataset to predict whether a person is malignant or benign. The performance indices of the proposed model are validated and the results exhibit its supremacy.
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Appendix 1
Appendix 1
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass [24]. They describe characteristics of the cell nuclei present in the image.
1.1 Attribute Information
(1) ID number
(2) Diagnosis (M = malignant, B = benign)
(3-32)
Ten real-valued features are computed for each cell nucleus:
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(a) Radius (mean of distances from center to points on the perimeter)
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(b) Texture (standard deviation of gray-scale values)
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(c) Perimeter
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(d) Area
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(e) Smoothness (local variation in radius lengths)
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(f) Compactness (perimeter^2 / area—1.0)
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(g) Concavity (severity of concave portions of the contour)
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(h) Concave points (number of concave portions of the contour)
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(i) Symmetry
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(j) Fractal dimension ("coastline approximation"—1)
Radius mean | Mean of distances from center to points on the perimeter |
Radius largest worst | Largest mean value for mean of distances from center to points on the perimeter |
Perimeter mean | Mean size of the core tumor |
Perimeter largest worst | Largest mean of perimeter |
Smoothness mean | Mean of local variation in radius lengths |
Compactness mean | Mean of perimeter^2 / area—1.0 |
Concavity mean | Mean of severity of concave portions of the contour |
Concave points mean | Mean for number of concave portions of the contour |
Symmetry mean | Symmetry |
Fractal dimension mean | Standard error for "coastline approximation" – 1 |
Radius se | Standard error for the mean of distances from center to points on the perimeter |
Texture se | Standard error for standard deviation of gray-scale values |
Perimeter se | Standard error for perimeter |
Area se | Standard error for area |
Smoothness se | Standard error for local variation in radius lengths |
Compactness se | Standard error for perimeter^2 / area—1.0 |
Concavity se | Standard error for severity of concave portions of the contour |
Concave points se | Mean for number of concave portions of the contour |
Symmetry se | Standard error for symmetry |
Fractal dimension se | Mean for "coastline approximation" – 1 |
Smoothness largest | "Worst" or largest mean value for local variation in radius lengths |
Texture largest worst | "Worst" or largest mean value for standard deviation of gray-scale values |
Concavity largest | "Worst" or largest mean value for severity of concave portions of the contour |
Area largest worst | Largest area |
Symmetry largest | Largest symmetry |
Compactness largest worst | "Worst" or largest mean value for perimeter^2 / area—1.0 |
Texture mean | Standard deviation of gray-scale values |
Concave points largest worst | "Worst" or largest mean value for number of concave portions of the contour |
Area mean | Mean of the area |
Fractal dimension largest worst | "Worst" or largest mean value for "coastline approximation"—1 |
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Hemavathi, N., Sriranjani, R., Arulmozhi, P. et al. Deep Learning based Early Prediction Scheme for Breast Cancer. Wireless Pers Commun 122, 931–946 (2022). https://doi.org/10.1007/s11277-021-08933-y
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DOI: https://doi.org/10.1007/s11277-021-08933-y