The document presents a new supervised approach for breast cancer diagnosis based on artificial social bees. It uses two algorithms: the social bees algorithm and the nearest neighbor (1-NN) algorithm. For each cell nucleus, 10 features are computed from medical data. The dataset contains 569 samples of benign and malignant cases. Artificial worker bees are used to classify the data based on different distance metrics, and the nearest neighbor algorithm is used for diagnosis.

1. A new supervised approach for breast cancer
diagnosis based on artificial social bees
Hanane MENAD
GeCoDe Laboratory
Department of Computer Science,
Tahar MOULAY University of Saida, Algeria
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 1 / 32

2. Contents
1 Introduction
2 Application of computer science in medicine
3 Approach Proposed
4 Results and Discussion
5 Conclusion and Perspective
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3. Introduction
Introduction
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4. Introduction
Introduction
Cancer is a chronic disease that can be fatal; it affects nowadays
increasingly the human worldwide.
The use of computers with automated tools, large volumes of
medical data are being collected and made available to the
medical research groups.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 4 / 32

5. Introduction
Introduction
Cancer is a chronic disease that can be fatal; it affects nowadays
increasingly the human worldwide.
The use of computers with automated tools, large volumes of
medical data are being collected and made available to the
medical research groups.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 4 / 32

6. Application of computer science in medicine
Application of computer
science in medicine
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7. Application of computer science in medicine
Application of computer science in medicine
The electronic devices supplied with processing units became an
important component of our everyday life.
Health care as a vital part of contemporary society model is also
affected by the same technical trends as the other branches of
business.
all computer methods that have proven to have technical and scientific
potential are quickly developed and utilized in medicine.
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8. Application of computer science in medicine
Application of computer science in medicine
Example results produced by the DMD system.
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9. Application of computer science in medicine
Application of computer science in medicine
Visualization of CTA volumetric dataset.
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10. Application of computer science in medicine
Application of computer science in medicine
The hardware set - up of prototype of virtual three-dimensional
desktop and picture taken during performance tests
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11. Application of computer science in medicine
Application of Computer Science in Medicine
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12. Application of computer science in medicine
Application of Computer Science in Medicine
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13. Application of computer science in medicine
Problematic
breast cancer presents a difficult issues for researchers, the main
issue is how can we diagnostic of cancer cellular ?
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14. Application of computer science in medicine
Proposed Solution
Our solution is based on suppervised classification using two
algorithms:
Social Bees Algorithm
the Nearest neighbhour (1-NN)
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15. Approach Proposed
Approach Proposed
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16. Approach Proposed
Approach Proposed
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17. Approach Proposed
Cell Nucleus
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18. Approach Proposed
Representation of Data
For each cell nucleus, 10 real-valued features are computed:
2 The perimeter.
3 Nuclear area.
4 The compactness of the cell nuclei (
perimeter2
area
− 1.0).
5 The smoothness of a nuclear contour (local variation in radius
lengths).
6 Concave points (number of concave portions of the contour).
7 Symmetry
8 The Concavity(severity of concave potions of the contour).
9 The fractal dimension of a cell (”coastline approximation” - 1).
10 The texture (standard deviation of gray-scale values).
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19. Approach Proposed
Representation of Data
For each cell nucleus, 10 real-valued features are computed:
1 Radius of individual nucleus.
2 The perimeter.
3 Nuclear area.
4 The compactness of the cell nuclei (
perimeter2
area
− 1.0).
5 The smoothness of a nuclear contour (local variation in radius
lengths).
6 Concave points (number of concave portions of the contour).
7 Symmetry
8 The Concavity(severity of concave potions of the contour).
9 The fractal dimension of a cell (”coastline approximation” - 1).
10 The texture (standard deviation of gray-scale values).
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20. Approach Proposed
Representation of Data
For each cell nucleus, 10 real-valued features are computed:
1 Radius of individual nucleus.
2 The perimeter.
3 Nuclear area.
4 The compactness of the cell nuclei (
perimeter2
area
− 1.0).
5 The smoothness of a nuclear contour (local variation in radius
lengths).
6 Concave points (number of concave portions of the contour).
7 Symmetry
8 The Concavity(severity of concave potions of the contour).
9 The fractal dimension of a cell (”coastline approximation” - 1).
10 The texture (standard deviation of gray-scale values).
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 17 / 32

21. Approach Proposed
Representation of Data
For each cell nucleus, 10 real-valued features are computed:
1 Radius of individual nucleus.
2 The perimeter.
3 Nuclear area.
4 The compactness of the cell nuclei (
perimeter2
area
− 1.0).
5 The smoothness of a nuclear contour (local variation in radius
lengths).
6 Concave points (number of concave portions of the contour).
7 Symmetry
8 The Concavity(severity of concave potions of the contour).
9 The fractal dimension of a cell (”coastline approximation” - 1).
10 The texture (standard deviation of gray-scale values).
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 17 / 32

22. Approach Proposed
Representation of Data
For each cell nucleus, 10 real-valued features are computed:
1 Radius of individual nucleus.
2 The perimeter.
3 Nuclear area.
4 The compactness of the cell nuclei (
perimeter2
area
− 1.0).
5 The smoothness of a nuclear contour (local variation in radius
lengths).
6 Concave points (number of concave portions of the contour).
7 Symmetry
8 The Concavity(severity of concave potions of the contour).
9 The fractal dimension of a cell (”coastline approximation” - 1).
10 The texture (standard deviation of gray-scale values).
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 17 / 32

23. Approach Proposed
Representation of Data
For each cell nucleus, 10 real-valued features are computed:
1 Radius of individual nucleus.
2 The perimeter.
3 Nuclear area.
4 The compactness of the cell nuclei (
perimeter2
area
− 1.0).
5 The smoothness of a nuclear contour (local variation in radius
lengths).
6 Concave points (number of concave portions of the contour).
7 Symmetry
8 The Concavity(severity of concave potions of the contour).
9 The fractal dimension of a cell (”coastline approximation” - 1).
10 The texture (standard deviation of gray-scale values).
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 17 / 32

24. Approach Proposed
Representation of Data
For each cell nucleus, 10 real-valued features are computed:
1 Radius of individual nucleus.
2 The perimeter.
3 Nuclear area.
4 The compactness of the cell nuclei (
perimeter2
area
− 1.0).
5 The smoothness of a nuclear contour (local variation in radius
lengths).
6 Concave points (number of concave portions of the contour).
7 Symmetry
8 The Concavity(severity of concave potions of the contour).
9 The fractal dimension of a cell (”coastline approximation” - 1).
10 The texture (standard deviation of gray-scale values).
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 17 / 32

25. Approach Proposed
Representation of Data
For each cell nucleus, 10 real-valued features are computed:
1 Radius of individual nucleus.
2 The perimeter.
3 Nuclear area.
4 The compactness of the cell nuclei (
perimeter2
area
− 1.0).
5 The smoothness of a nuclear contour (local variation in radius
lengths).
6 Concave points (number of concave portions of the contour).
7 Symmetry
8 The Concavity(severity of concave potions of the contour).
9 The fractal dimension of a cell (”coastline approximation” - 1).
10 The texture (standard deviation of gray-scale values).
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 17 / 32

26. Approach Proposed
Representation of Data
For each cell nucleus, 10 real-valued features are computed:
1 Radius of individual nucleus.
2 The perimeter.
3 Nuclear area.
4 The compactness of the cell nuclei (
perimeter2
area
− 1.0).
5 The smoothness of a nuclear contour (local variation in radius
lengths).
6 Concave points (number of concave portions of the contour).
7 Symmetry
8 The Concavity(severity of concave potions of the contour).
9 The fractal dimension of a cell (”coastline approximation” - 1).
10 The texture (standard deviation of gray-scale values).
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 17 / 32

27. Approach Proposed
Representation of Data
For each cell nucleus, 10 real-valued features are computed:
1 Radius of individual nucleus.
2 The perimeter.
3 Nuclear area.
4 The compactness of the cell nuclei (
perimeter2
area
− 1.0).
5 The smoothness of a nuclear contour (local variation in radius
lengths).
6 Concave points (number of concave portions of the contour).
7 Symmetry
8 The Concavity(severity of concave potions of the contour).
9 The fractal dimension of a cell (”coastline approximation” - 1).
10 The texture (standard deviation of gray-scale values).
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 17 / 32

28. Approach Proposed
Representation of Data
For each cell nucleus, 10 real-valued features are computed:
1 Radius of individual nucleus.
2 The perimeter.
3 Nuclear area.
4 The compactness of the cell nuclei (
perimeter2
area
− 1.0).
5 The smoothness of a nuclear contour (local variation in radius
lengths).
6 Concave points (number of concave portions of the contour).
7 Symmetry
8 The Concavity(severity of concave potions of the contour).
9 The fractal dimension of a cell (”coastline approximation” - 1).
10 The texture (standard deviation of gray-scale values).
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 17 / 32

29. Approach Proposed
Dataset
the Wisconsin Diagnostic Breast Cancer represented by 30
element vector containing 30 real values.
This dataset contains 569 samples:
357 examples present Benign.
212 examples present Malignant.
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30. Approach Proposed
Social Worker Bees
Main tasks of natural worker bees:
Worker Bee Undertakers (days 3 to 16).
Collecting Nectar for the Hive (days 12 to 18).
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31. Approach Proposed
Social Worker Bees
Main tasks of natural worker bees:
Worker Bee Housekeeping (days 1 to 3).
Worker Bee Undertakers (days 3 to 16).
Collecting Nectar for the Hive (days 12 to 18).
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32. Approach Proposed
Social Worker Bees
Main tasks of natural worker bees:
Worker Bee Housekeeping (days 1 to 3).
Worker Bee Undertakers (days 3 to 16).
Collecting Nectar for the Hive (days 12 to 18).
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33. Approach Proposed
Social Worker Bees
Main tasks of natural worker bees:
Worker Bee Housekeeping (days 1 to 3).
Worker Bee Undertakers (days 3 to 16).
Collecting Nectar for the Hive (days 12 to 18).
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34. Approach Proposed
Artificial Worker Bees
Used distances:
Manhattan (Man) :
D(x,y) = Σ|xi-yi|
Euclidean (Euc):
D(x,y) = Σ (xi − yi)2
Chebyshev (Cheb):
D(x, y)= Max(|xi-yi|)
Cosine (Cos) :
D(X, Y ) =
Σxi × yi
Σx2
i × Σy2
i
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35. Approach Proposed
Artificial Worker Bees
Used distances:
Manhattan (Man) :
D(x,y) = Σ|xi-yi|
Euclidean (Euc):
D(x,y) = Σ (xi − yi)2
Chebyshev (Cheb):
D(x, y)= Max(|xi-yi|)
Cosine (Cos) :
D(X, Y ) =
Σxi × yi
Σx2
i × Σy2
i
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 20 / 32

36. Approach Proposed
Artificial Worker Bees
Used distances:
Manhattan (Man) :
D(x,y) = Σ|xi-yi|
Euclidean (Euc):
D(x,y) = Σ (xi − yi)2
Chebyshev (Cheb):
D(x, y)= Max(|xi-yi|)
Cosine (Cos) :
D(X, Y ) =
Σxi × yi
Σx2
i × Σy2
i
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 20 / 32

37. Approach Proposed
Artificial Worker Bees
Used distances:
Manhattan (Man) :
D(x,y) = Σ|xi-yi|
Euclidean (Euc):
D(x,y) = Σ (xi − yi)2
Chebyshev (Cheb):
D(x, y)= Max(|xi-yi|)
Cosine (Cos) :
D(X, Y ) =
Σxi × yi
Σx2
i × Σy2
i
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 20 / 32

38. Approach Proposed
Normalisation of distances
In order to give the distances the same impact, we used to normalise
Euclidean, Manhattan, and Chebyshev distances as follow:
Di(x, y) =
Di(x, y) − DMin(x, y)
DMax(x, y) − DMin(x, y)
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39. Approach Proposed
Classification
We calculated the average distance of three calculated distances
(tasks):
Daverage(x, y) =
D1(X, Y ) + D2(X, Y ) + D3(X, Y )
3
then we classified according to the minimum distance average.
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40. Approach Proposed
Evaluation
To evaluate our approach, we calculated the following metrics:
Accuracy (AC).
Error (Er).
Precision (Pr).
Recall (Rc).
F-measure.
Entropy.
Duration.
Confusion Matrix.
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41. Approach Proposed
Evaluation
To evaluate our approach, we calculated the following metrics:
Accuracy (AC).
Error (Er).
Precision (Pr).
Recall (Rc).
F-measure.
Entropy.
Duration.
Confusion Matrix.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 23 / 32

42. Approach Proposed
Evaluation
To evaluate our approach, we calculated the following metrics:
Accuracy (AC).
Error (Er).
Precision (Pr).
Recall (Rc).
F-measure.
Entropy.
Duration.
Confusion Matrix.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 23 / 32

43. Approach Proposed
Evaluation
To evaluate our approach, we calculated the following metrics:
Accuracy (AC).
Error (Er).
Precision (Pr).
Recall (Rc).
F-measure.
Entropy.
Duration.
Confusion Matrix.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 23 / 32

44. Approach Proposed
Evaluation
To evaluate our approach, we calculated the following metrics:
Accuracy (AC).
Error (Er).
Precision (Pr).
Recall (Rc).
F-measure.
Entropy.
Duration.
Confusion Matrix.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 23 / 32

45. Approach Proposed
Evaluation
To evaluate our approach, we calculated the following metrics:
Accuracy (AC).
Error (Er).
Precision (Pr).
Recall (Rc).
F-measure.
Entropy.
Duration.
Confusion Matrix.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 23 / 32

46. Approach Proposed
Evaluation
To evaluate our approach, we calculated the following metrics:
Accuracy (AC).
Error (Er).
Precision (Pr).
Recall (Rc).
F-measure.
Entropy.
Duration.
Confusion Matrix.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 23 / 32

47. Approach Proposed
Evaluation
To evaluate our approach, we calculated the following metrics:
Accuracy (AC).
Error (Er).
Precision (Pr).
Recall (Rc).
F-measure.
Entropy.
Duration.
Confusion Matrix.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 23 / 32

48. Approach Proposed
Evaluation
To evaluate our approach, we calculated the following metrics:
Accuracy (AC).
Error (Er).
Precision (Pr).
Recall (Rc).
F-measure.
Entropy.
Duration.
Confusion Matrix.
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49. Results and Discussion
Results and Discussion
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50. Results and Discussion
Experiments conducted using Social Worker Bees
• Experiment1 (Ex1): Euclidean + Manhattan + Chebyshev.
• Experiment2 (Ex2): Euclidean + Manhattan + Cosine.
• Experiment3 (Ex3): Euclidean + Chebyshev + Cosine.
• Experiment4 (Ex4): Manhattan + Cosine + Chebyshev.
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51. Results and Discussion
Experiments conducted using Social Worker Bees
• Experiment1 (Ex1): Euclidean + Manhattan + Chebyshev.
• Experiment2 (Ex2): Euclidean + Manhattan + Cosine.
• Experiment3 (Ex3): Euclidean + Chebyshev + Cosine.
• Experiment4 (Ex4): Manhattan + Cosine + Chebyshev.
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52. Results and Discussion
Experiments conducted using Social Worker Bees
• Experiment1 (Ex1): Euclidean + Manhattan + Chebyshev.
• Experiment2 (Ex2): Euclidean + Manhattan + Cosine.
• Experiment3 (Ex3): Euclidean + Chebyshev + Cosine.
• Experiment4 (Ex4): Manhattan + Cosine + Chebyshev.
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53. Results and Discussion
Experiments conducted using Social Worker Bees
• Experiment1 (Ex1): Euclidean + Manhattan + Chebyshev.
• Experiment2 (Ex2): Euclidean + Manhattan + Cosine.
• Experiment3 (Ex3): Euclidean + Chebyshev + Cosine.
• Experiment4 (Ex4): Manhattan + Cosine + Chebyshev.
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54. Results and Discussion
Obtained Results by Social Worker Bees
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55. Results and Discussion
Obtained Results by Nearest Neighbour (1-NN)
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56. Conclusion and Perspective
Conclusion and Perspective
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57. Conclusion and Perspective
Conclusion
Computers have become an essential part of every hospital. These
machines help in carrying out these tasks and medical procedures
much more efficiently and effectively.
The increasing complexity of real-world problems motivates the
researchers to search for efficient methods. Divide and conquer
techniques are the one way to solve large and complex problems
which has been a practice in research since long time.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 29 / 32

58. Conclusion and Perspective
Conclusion
Computers have become an essential part of every hospital. These
machines help in carrying out these tasks and medical procedures
much more efficiently and effectively.
The increasing complexity of real-world problems motivates the
researchers to search for efficient methods. Divide and conquer
techniques are the one way to solve large and complex problems
which has been a practice in research since long time.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 29 / 32

59. Conclusion and Perspective
Conclusion
We have introduced an approach inspired from Social Worker Bees
that proves that it is a good source to inspire from it, with its
mechanism and based on multi agent task.
Our approach shows many advantages
Easy to understand
Easy to implement because it is based on distance calculation.
The main advantage of this approach is its efficiency
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 30 / 32

60. Conclusion and Perspective
Conclusion
We have introduced an approach inspired from Social Worker Bees
that proves that it is a good source to inspire from it, with its
mechanism and based on multi agent task.
Our approach shows many advantages
Easy to understand
Easy to implement because it is based on distance calculation.
The main advantage of this approach is its efficiency
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 30 / 32

61. Conclusion and Perspective
Conclusion
We have introduced an approach inspired from Social Worker Bees
that proves that it is a good source to inspire from it, with its
mechanism and based on multi agent task.
Our approach shows many advantages
Easy to understand
Easy to implement because it is based on distance calculation.
The main advantage of this approach is its efficiency
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 30 / 32

62. Conclusion and Perspective
Conclusion
We have introduced an approach inspired from Social Worker Bees
that proves that it is a good source to inspire from it, with its
mechanism and based on multi agent task.
Our approach shows many advantages
Easy to understand
Easy to implement because it is based on distance calculation.
The main advantage of this approach is its efficiency
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 30 / 32

63. Conclusion and Perspective
Future Works
Generate the approach to diagnostic of other cancer types.
Combine this approach with data mining techniques that prove its
effiency such as K-NN we used in this study.
Use other bio-inspired methods for this task.
Use it for different medical applications.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 31 / 32

64. Conclusion and Perspective
Future Works
Generate the approach to diagnostic of other cancer types.
Combine this approach with data mining techniques that prove its
effiency such as K-NN we used in this study.
Use other bio-inspired methods for this task.
Use it for different medical applications.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 31 / 32

65. Conclusion and Perspective
Future Works
Generate the approach to diagnostic of other cancer types.
Combine this approach with data mining techniques that prove its
effiency such as K-NN we used in this study.
Use other bio-inspired methods for this task.
Use it for different medical applications.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 31 / 32

66. Conclusion and Perspective
Future Works
Generate the approach to diagnostic of other cancer types.
Combine this approach with data mining techniques that prove its
effiency such as K-NN we used in this study.
Use other bio-inspired methods for this task.
Use it for different medical applications.
Hanane MENAD A new supervised approach for breast cancer diagnosis based on artificial social bees 31 / 32

67. That’s all. Thanks for your attention! Any Questions?
Hanane MENAD
GeCoDe Laboratory, Department of Computer Science,
Tahar MOULAY University of Saida, Algeria
Email: hananemenad92@gmail.com
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