This document discusses using machine learning models to classify breast cancer tumors as benign or malignant based on cell nucleus characteristics without biopsy. It summarizes loading and preprocessing the Wisconsin Breast Cancer dataset, performing explanatory data analysis to identify important features, engineering features, training classifiers including SVC and evaluating models. SHAP and permutation feature importance analysis identified concave point characteristics as most important for classification. The top performing SVC classifier achieved over 99% accuracy, allowing diagnosis without biopsy. Future work could apply these methods to other cancers where biopsy is difficult.

1. Wisconsin Breast Cancer
dataset
GUGC
Da Hee Kim
Advised by Homin Park

2. Contents
• Introduction
• Loading & checking the data
• Explanatory Data analysis (EDA)
• Feature Engineering
• Modeling
• Interpretability/ Explainable AI (XAI)
• Wrap-up
• Future research

3. 1. Importance

4. 1. Introduction
- The problem
Christina
Applegate
Sharon
Osbourne
Angelina
Jolie

5. 1. Introduction
- The problem
The 10 leading types of estimated new cancer cases and deaths in 2020. (South Korea)
(A) Estimated new cases

6. 1. Introduction
- The problem
The 10 leading types of estimated new cancer cases and deaths in 2020. (South Korea)
(A) Estimated new cases (B) Estimated Deaths

7. Cancer ≠Tumor: Abnormal growth of cells causing a mass of tissue
• Malignant tumors are
cancerous and invade other
sites.
• Benign tumors stay in their
primary location.
1. Introduction
- The problem

8. 1. Introduction
- The problem: Diagnosis

9. 1. Introduction
- The problem
Benign Malignant
• Nucleus size uniform
• Symmetrical
• Homogenous
• Areas within normal size
• Non uniform nucleus
• Asymmetrical
• Non homogenous sizes
• Areas above normal size

10. 1. Introduction
- The problem: Diagnosis
Problem:
 Depending on type, painful to patient
 Potential side effects (ex: bruising)
 Diagnosis can take time
 Tedious process
Model Malignant
Benign
Machine learning
Imaging

11. 1. Wisconsin Breast Cancer dataset
- What?
• Has parameters measured form a fine needle aspirate of a breast mass.
• The parameters are about the cell nucleus. (569 cells)
Cell Nuclei

12. 1. Wisconsin Breast Cancer dataset
- What?
• The parameters include the following 10 features
•Radius
•Texture
•Perimeter
•Area
•Smoothness
•Compactness (perimeter^2 / area - 1.0)
•Concavity (severity of concave portions of the contour)
•Concave points (number of concave portions of the contour)
•Symmetry
•Fractal dimension

13. 1. Wisconsin Breast Cancer dataset
- What?
• The parameters include the following 10 features
•Texture: standard deviation of gray-scale values
•Smoothness: local variation in radius lengths
•Compactness: (perimeter^2 / area - 1.0)
•Concavity: severity of concave portions of the contour
•Concave points: Number of concave portions of the contour
•Symmetry: Uses nucleus deformation parameter to measure
how non-spherical a nucleus is.
• Fractal dimension: ("coastline approximation" - 1)
Radius
Perimeter
Area
Nuclei

14. 1. Wisconsin Breast Cancer dataset
- What? : Structure of dataset
• The parameters include the following 10 features
Mean
Standard error
Worst
30 features
• ID
• diagnosis
33 Columns
Feature
10
• Unnamed

15. • Doctors will be able to determine whether the tumor is malignant or benign through imaging
without biopsy.
• Breakthroughs with breast cancer can act as a steppingstone for other cancers where biopsy is
difficult to conduct.
1. Wisconsin Breast Cancer dataset
- Importance?
Model Malignant
Benign

16. 2. Machine learning process

17. 1. Loading and checking data
1: Loading data
2: Checking for null values
3. Outlier detection
4. Summary and statistics

18. 1. Loading and checking the data

19. 1. Loading and checking the data
ID & Diagnosis
Mean
Standard error
Worst
Unnamed

20. 1. Loading and checking the data
- 2: Checking for null values
• All the data in the Unnamed column consists of null
values
• We will thus remove this column later

21. 1. Loading and checking the data
- 3: Outlier Detection
• We will drop these later
• Redefine “X” which includes only the features
• Output: Outliers found depending on only the feature traits (x_col)

22. 1. Loading and checking the data
- 4: Summary and statistics
• We can observe the statistical values for each of the features
• Redefine “X” which includes only the features
• Output

23. 1. Loading and checking the data
- 4: Summary and statistics
• We can observe the quantity of each benign and malignant tumors
• Redefine “data_w_diag” which includes the diagnosis and the 30 features
• Output
Number of Benign: 357
Number of Malignant: 212

24. 2. Explanatory Data Analysis
1: Heat Map all features
2: Important features
2-1: Radius VS Perimeter VS Area
1: Heat map
2-2: Compactness VS Concavity VS Compactness
1: Heat map
2: Feature plotting: Histogram
3: Overall data distribution

25. 2. EDA
- 1: Heat Map
We can see a couple of relations using the heat map.
1. Within the mean or worst features, we can see that
radius is highly correlated to the perimeter and the area.
2. compactness, concavity and concave points are
corelated to each other.

26. 2. EDA
- 2-1: Radius VS Perimeter VS Area
1. Heat map
• Different colors: 1.0 is due to the rounding
of correlation values.

27. 2. EDA
-2-2: Compactness VS Concave points VS Concavity
1. Heat map
We will compare the following
features
• Compactness & concavity & concave points
 High correlation
• Compactness mean VS compactness worst
• Concavity mean VS concavity worst
• Concave points mean VS concave points
worst

28. 2. EDA
-2-2: Compactness VS Concave points VS Concavity
2. Feature plotting: Worst VS Mean
• Concave points
• Concavity
• Compactness
• concavity & compactness:
worst ≒ mean
• Concave points:
worst ≠ mean
Similarity of overall distribution

29. 2. EDA
- 3: Data distribution
• Violin Plot
• Worst
• Mean • Standard Error

30. 2. EDA
- 3: Data distribution
• Violin Plot
• Worst
• Red box: Examples of features with good
separation
• Blue box: Examples of features with bad
separation
Assume that features with good separation
will have higher feature importance

31. 3. Feature Engineering
1: Standardization
2: Outlier detection

32. 3. Feature engineering
- 1. Standardization
Before
After

33. 3. Feature engineering
- 2. Outlier Deletion: Swarm Plot
Before
After

34. 4. Modeling
1: Splitting data
2: Classification
2-1: ANN
2-2: SVC vs Decision Tree vs Ada Boost vs Random forest vs Extra trees vs GBC vs Logistic regression
3: Cross validate models
4: Hyper parameter tuning
5: Evaluating models

35. 4. Modeling
-1: Splitting data

36. 4. Modeling
-2: Classification
• ANN
Parameters
• Hidden layers: 3
• Optimizer: ADAMS
• Learning rate: 0.003
• Epoch: 200

37. 4. Modeling
-3: Cross validate models
• SVC VS Decision Tree VS Ada Boost VS Random forest VS Extra trees VS GBC VS Logistic regression

38. 4. Modeling
-4: Hyperparameter tuning for the selected models
• SVC
• Extra trees • GBC
• Logistic regression

39. Model Accuracy Precision Recall F-1 Score
ANN 0.978 0.95 0.974 0.962
SVC classifier 0.992 0.974 1.000 0.987
Extra Trees
classifier
0.978 0.974 0.950 0.962
Gradient boosting
classifier
0.957 0.923 0.923 0.923
Logistic regression 0.985 0.974 0.974 0.974
4. Modeling
-5: Evaluating models
• Sensitivity (recall) is the most important aspect.
 less misclassifications of diagnosing a cancer patient as negative.
• SVC: has highest for all parameters.
 SVC is effective for data with clear distinction.
 The dataset may be too small for ANN to have best results.
• All models have a value greater than 0.9.
 Assume that there exists a feature which allows for good classification.

40. 5. Explainable AI
1: SHAP
1-1: Summary plot
2: Permutation Feature Importance
3: Comparison (SHAP VS PFI)

41. 5. Explainable AI
- 1: SHAP
1. Summary plot

42. 5. Explainable AI
- 1: SHAP
3. Summary plot
• Concave point_worst has highest importance
• Features that had high correlation in EDA
• Concave points vs concavity vs compactness
 Concave points ≠ concavity ≠ compactness
• Area vs Perimeter vs radius
 Area_worst ≒ radius_worst ≠ perimeter_worst
 Area_mean ≠ radius_mean ≒ perimeter_mean

43. 5. Explainable AI
- 2: Permeative feature importance

44. 5. Explainable AI
- 3: Comparing Output
SHAP VS PFI
• Interactions: PFI calculates decrease in model performance when a feature
is permuted while SHAP values account for the interaction between featur
e & captures non linear relationships.
 For complex non-linear models, SHAP may provide better ranking
o We used ‘rbf’ as the kernel function. SHAP can handle the non-lin
earities  PFI may not totally capture the non linearities.
 Some features showed high interactions. In PFI this is not considered.
o may cause difference in importance
• Distribution differences: SHAP considers the distribution of the entire data
set while PFI focuses on the effect of permuting a feature individually.

45. Wrap-up
• We can determine whether a tumor is benign or malignant using SVC classifier with
high accuracy.
Implementation: No biopsy would be needed for diagnosis

46. Future work
• Methods for imaging to scale would be needed.
• There are other cancers such as kidney, in which biopsy is much more difficult.
Could use the breast cancer model as stepping stones for advancements in those
areas
o No biopsy would be needed, and diagnosis would be less tedious and painful
to the patients.
o Human labor can be reduced.