A Novel Approach for Breast Cancer Detection using Data Mining Techniques

A Novel Approach for Breast Cancer
Detection using
Data Mining Techniques
Presented by:
• Ahmed Abd Elhafeez
15/7/2014 AAST-Comp eng

AGENDA
 Scientific and Medical Background
1. What is cancer?
2. Breast cancer
3. History and Background
4. Pattern recognition system decomposition
5. About data mining
6. Data mining tools
7. Classification Techniques
2 5/7/2014AAST-Comp eng

AGENDA (Cont.)
 Paper contents
1. Introduction
2. Related Work
3. Classification Techniques
4. Experiments and Results
5. Conclusion
6. References

What Is Cancer?
 Cancer is a term used for diseases in which abnormal
cells divide without control and are able to invade
other tissues. Cancer cells can spread to other parts of
the body through the blood and lymph systems.
 Cancer is not just one disease but many diseases.
There are more than 100 different types of cancer.
 Most cancers are named for the organ or type of cell in
which they start
 There are two general types of cancer tumours namely:
• benign
• malignant
4 AAST-Comp eng 5/7/2014

Skin
cancer
Breast cancerColon cancer
Lung cancer
Pancreatic cancer
Liver Bladder
Prostate Cancer
Kidney cancerThyroid Cancer
Leukemia Cancer
Edometrial Cancer
Rectal Cancer
Non-Hodgkin Lymphoma
Cervical cancer
Thyroid Cancer
Oral cancer
AAST-Comp eng 55/7/2014

Breast Cancer
6
• The second leading cause of death among
women is breast cancer, as it comes
directly after lung cancer.
• Breast cancer considered the most
common invasive cancer in women, with
more than one million cases and nearly
600,000 deaths occurring worldwide
annually.
• Breast cancer comes in the top of cancer
list in Egypt by 42 cases per 100 thousand
of the population. However 80% of the
cases of breast cancer in Egypt are of the
benign kind.
AAST-Comp eng5/7/2014

History and Background
Medical Prognosis is the estimation of :
• Cure
• Complication
• disease recurrence
• Survival
for a patient or group of patients after treatment.
7AAST-Comp eng5/7/2014

Breast Cancer Classification
8AAST-Comp eng
Round well-
defined, larger
groups are more
likely benign.
Tight cluster of tiny,
irregularly shaped
groups may indicate
cancer Malignant
Suspicious pixels groups show up as white spots on a
mammogram.
5/7/2014

Breast cancer’s Features
• MRI - Cancer can have a unique appearance –
features that turned out to be cancer used for diagnosis
/ prognosis of each cell nucleus.
9AAST-Comp eng
F2Magnetic
Resonance Image
F1
F3
Fn
Feature
Extraction
5/7/2014

Diagnosis or prognosis
Brest Cancer
Benign
Malignant

Computer-Aided Diagnosis
• Mammography allows for efficient diagnosis of
breast cancers at an earlier stage
• Radiologists misdiagnose 10-30% of the malignant
cases
• Of the cases sent for surgical biopsy, only 10-20%
are actually malignant
5/7/2014 AAST-Comp eng 11

Computational Intelligence
Computational Intelligence
Data + Knowledge
Artificial Intelligence
Expert
systems
Fuzzy
logic
Pattern
Recognition
Machine
learning
Probabilistic
methods
Multivariate
statistics
Visuali-
zation
Evolutionary
algorithms
Neural
networks

What do these methods do?
• Provide non-parametric models of data.
• Allow to classify new data to pre-defined
categories, supporting diagnosis &
prognosis.
• Allow to discover new categories.
• Allow to understand the data, creating fuzzy
or crisp logical rules.
• Help to visualize multi-dimensional
relationships among data samples.5/7/2014 AAST-Comp eng 13

Feature selection
Data Preprocessing
Selecting Data mining tooldataset
Classification algorithm
SMO IBK BF TREE
Results and evaluations
AAST-Comp eng
Pattern recognition system decomposition
5/7/2014

Results
Data
preprocessing
Feature selectionClassification
Selection tool
data mining
Performance evaluation Cycle
Dataset

data sets

results
Data
preprocessing
Feature selectionclassification
Selection tool
datamining
Dataset

AAST-Comp eng 18
Data Mining
• Data Mining is set of techniques used
in various domains to give meaning to
the available data
• Objective: Fit data to a model
–Descriptive
–Predictive
5/7/2014

Predictive & descriptive data mining
• Predictive:
Is the process of automatically creating a classification
model from a set of examples, called the training set,
which belongs to a set of classes.
Once a model is created, it can be used to automatically
predict the class of other unclassified examples
• Descriptive :
Is to describe the general or special features of a set of
data in a concise manner

AAST-Comp eng 20
Data Mining Models and Tasks
5/7/2014

Data mining Tools
Many advanced tools for data mining are
available either as open-source or commercial
software.
21AAST-Comp eng5/7/2014

weka
• Waikato environment for knowledge analysis
• Weka is a collection of machine learning algorithms for
data mining tasks. The algorithms can either be applied
directly to a dataset or called from your own Java code.
• Weka contains tools for data pre-processing,
classification, regression, clustering, association rules,
and visualization. It is also well-suited for developing
new machine learning schemes.
• Found only on the islands of New Zealand, the Weka is
a flightless bird with an inquisitive nature.

Results
Data
preprocessing
Feature
selection Classification
Selection tool
data mining
Dataset

Data Preprocessing
• Data in the real world is :
– incomplete: lacking attribute values, lacking certain attributes
of interest, or containing only aggregate data
– noisy: containing errors or outliers
– inconsistent: containing discrepancies in codes or names
• Quality decisions must be based on quality data
measures:
Accuracy ,Completeness, Consistency, Timeliness, Believability,
Value added and Accessibility

Preprocessing techniques
• Data cleaning
– Fill in missing values, smooth noisy data, identify or remove outliers and
resolve inconsistencies
• Data integration
– Integration of multiple databases, data cubes or files
• Data transformation
– Normalization and aggregation
• Data reduction
– Obtains reduced representation in volume but produces the same or
similar analytical results
• Data discretization
– Part of data reduction but with particular importance, especially for
numerical data

Results
Data
preprocessing
Feature
selection
Classification
Selection tool
datamining
Dataset

Finding a feature subset that has the most
discriminative information from the original
feature space.
The objective of feature selection is :
• Improving the prediction performance of the
predictors
• Providing a faster and more cost-effective
predictors
• Providing a better understanding of the underlying
process that generated the data
Feature selection

Feature Selection
• Transforming a dataset by removing some
of its columns
A1 A2 A3 A4 C A2 A4 C

Results
Data
preprocessing
Feature
selection
Classification
Selection tool
data mining
Dataset

Supervised Learning
• Supervision: The training data (observations, measurements, etc.) are
accompanied by labels indicating the class of the observations
• New data is classified based on the model built on training set
known categories
AAST-Comp eng
Category ―A‖
Category ―B‖
Classification (Recognition)
(Supervised Classification)
305/7/2014

Classification
• Everyday, all the time we classify
things.
• Eg crossing the street:
– Is there a car coming?
– At what speed?
– How far is it to the other side?
– Classification: Safe to walk or not!!!

 Classification:
 predicts categorical class labels (discrete or nominal)
 classifies data (constructs a model) based on the
training set and the values (class labels) in a
classifying attribute and uses it in classifying new data
 Prediction:
 models continuous-valued functions, i.e., predicts
unknown or missing values
Classification vs. Prediction

Classification—A Two-Step Process
 Model construction: describing a set of predetermined classes
 Each tuple/sample is assumed to belong to a predefined class,
as determined by the class label attribute
 The set of tuples used for model construction is training set
 The model is represented as classification rules, decision trees,
or mathematical formulae
 Model usage: for classifying future or unknown objects
 Estimate accuracy of the model
 The known label of test sample is compared with the
classified result from the model
 Accuracy rate is the percentage of test set samples that are
correctly classified by the model
 Test set is independent of training set, otherwise over-fitting
will occur
 If the accuracy is acceptable, use the model to classify data
tuples whose class labels are not known

Classification Process (1): Model
Construction
Training
Data
N A M E R A N K Y E A R S T E N U R E D
M ik e A s s is ta n t P ro f 3 n o
M a ry A s s is ta n t P ro f 7 ye s
B ill P ro fe s s o r 2 ye s
J im A s s o c ia te P ro f 7 ye s
D a ve A s s is ta n t P ro f 6 n o
A n n e A s s o c ia te P ro f 3 n o
Classification
Algorithms
IF rank = „professor‟
OR years > 6
THEN tenured = „yes‟
Classifier
(Model)

Classification Process (2): Use the
Model in Prediction
Classifier
Testing
Data
N A M E R A N K Y E A R S T E N U R E D
T o m A s s is ta n t P ro f 2 n o
M e rlis a A s s o c ia te P ro f 7 n o
G e o rg e P ro fe s s o r 5 ye s
J o s e p h A s s is ta n t P ro f 7 ye s
Unseen Data
(Jeff, Professor, 4)
Tenured?

Classification
• is a data mining (machine learning) technique used to
predict group membership for data instances.
• Classification analysis is the organization of data in
given class.
• These approaches normally use a training set where
all objects are already associated with known class
labels.
• The classification algorithm learns from the training
set and builds a model.
• Many classification models are used to classify new
objects.

Classification
• predicts categorical class labels (discrete or
nominal)
• constructs a model based on the training set
and the values (class labels) in a classifying
attribute and uses it in classifying unseen
data

Quality of a classifier
• Quality will be calculated with respect to lowest
computing time.
• Quality of certain model one can describe by confusion
matrix.
• Confusion matrix shows a new entry properties
predictive ability of the method.
• Row of the matrix represents the instances in a
predicted class, while each column represents the
instances in an actual class.
• Thus the diagonal elements represent correctly
classified compounds
• the cross-diagonal elements represent misclassified
compounds.

Classification Techniques
 Building accurate and efficient classifiers for
large databases is one of the essential tasks of
data mining and machine learning research
 The ultimate reason for doing classification is to
increase understanding of the domain or to
improve predictions compared to unclassified
data.
5/7/2014AAST-Comp eng39

Classification Techniques
classificatio
n
Techniques
Naïve
Bays
SVM
C4.5
KNN
BF tree
IBK

Classification Model
Support vector machine
Classifier
V. Vapnik

Support Vector Machine (SVM)
 SVM is a state-of-the-art learning machine which has
been extensively used as a tool for data
classification , function approximation, etc.
 due to its generalization ability and has found a
great deal of success in many applications.
 Unlike traditional methods which minimizing the
empirical training error, a noteworthy feature of SVM
is that it minimize an upper bound of the
generalization error through maximizing the margin
between the separating hyper-plane and a data set

 SVM is a state-of-the-art learning machine which has
been extensively used as a tool for data
classification , function approximation, etc.
 due to its generalization ability and has found a
great deal of success in many applications.
 Unlike traditional methods which minimizing the
empirical training error, a noteworthy feature of SVM
is that it minimize an upper bound of the
generalization error through maximizing the margin
between the separating hyper-plane and a data set

Tennis example
Humidity
Temperature
= play tennis
= do not play tennis

Linear classifiers: Which Hyperplane?
• Lots of possible solutions for a, b, c.
• Some methods find a separating hyperplane,
but not the optimal one
• Support Vector Machine (SVM) finds an
optimal solution.
– Maximizes the distance between the
hyperplane and the “difficult points” close to
decision boundary
– One intuition: if there are no points near the
decision surface, then there are no very
uncertain classification decisions
45
This line
represents the
decision
boundary:
ax + by − c = 0
Ch. 15

Selection of a Good Hyper-Plane
Objective: Select a `good' hyper-plane using
only the data!
Intuition:
(Vapnik 1965) - assuming linear separability
(i) Separate the data
(ii) Place hyper-plane `far' from data

SVM – Support Vector Machines
Support Vectors
Small Margin Large Margin

• SVMs maximize the margin around
the separating hyperplane.
• The decision function is fully
specified by a subset of training
samples, the support vectors.
• Solving SVMs is a quadratic
programming problem
• Seen by many as the most
successful current text
classification method
48
Support vectors
Maximizes
margin
Sec. 15.1
Narrower
margin

Non-Separable Case
The Lagrangian trick

SVM
 SVM
 Relatively new concept
 Nice Generalization properties
 Hard to learn – learned in batch mode using
quadratic programming techniques
 Using kernels can learn very complex functions

Classification Model
K-Nearest Neighbor
Classifier5/7/2014 AAST-Comp eng 52

K-Nearest Neighbor Classifier
Learning by analogy:
Tell me who your friends are and I’ll tell
you who you are
A new example is assigned to the most
common class among the (K) examples
that are most similar to it.

K-Nearest Neighbor Algorithm
 To determine the class of a new example E:
 Calculate the distance between E and all examples in
the training set
 Select K-nearest examples to E in the training set
 Assign E to the most common class among its K-
nearest neighbors
Response
Response
No response
No response
No response
Class: Response

 Each example is represented with a set of numerical
attributes
 ―Closeness‖ is defined in terms of the Euclidean distance
between two examples.
 The Euclidean distance between X=(x1, x2, x3,…xn) and Y
=(y1,y2, y3,…yn) is defined as:
 Distance (John, Rachel)=sqrt [(35-41)2+(95K-215K)2 +(3-2)2]
n
i
ii
yxYXD
1
2
)(),(
John:
Age=35
Income=95K
No. of credit cards=3
Rachel:
Age=41
Income=215K
No. of credit cards=2
Distance Between Neighbors

Instance Based Learning
 No model is built: Store all training examples
 Any processing is delayed until a new instance must be
classified.
Response
Response No response
No response
No response
Class: Respond

Example : 3-Nearest Neighbors
Customer Age Income No. credit
cards
Response
John 35 35K 3 No
Rachel 22 50K 2 Yes
Hannah 63 200K 1 No
Tom 59 170K 1 No
Nellie 25 40K 4 Yes
David 37 50K 2 ?

Customer Age Income
(K)
No.
cards
John 35 35 3
Rachel 22 50 2
Hannah 63 200 1
Tom 59 170 1
Nellie 25 40 4
David 37 50 2
Response
No
Yes
No
No
Yes
Distance from David
sqrt [(35-37)2+(35-50)2
+(3-2)2]=15.16
sqrt [(22-37)2+(50-50)2
+(2-2)2]=15
sqrt [(63-37)2+(200-
50)2 +(1-2)2]=152.23
sqrt [(59-37)2+(170-
50)2 +(1-2)2]=122
sqrt [(25-37)2+(40-50)2
+(4-2)2]=15.74
Yes

Strengths and Weaknesses
Strengths:
 Simple to implement and use
 Comprehensible – easy to explain prediction
 Robust to noisy data by averaging k-nearest neighbors.
Weaknesses:
 Need a lot of space to store all examples.
 Takes more time to classify a new example than with a
model (need to calculate and compare distance from
new example to all other examples).

Decision Tree

– Decision tree induction is a simple but powerful
learning paradigm. In this method a set of training
examples is broken down into smaller and smaller
subsets while at the same time an associated decision
tree get incrementally developed. At the end of the
learning process, a decision tree covering the training
set is returned.
– The decision tree can be thought of as a set sentences
written propositional logic.

Example
Jenny Lind is a writer of romance novels. A movie
company and a TV network both want exclusive
rights to one of her more popular works. If she signs
with the network, she will receive a single lump sum,
but if she signs with the movie company, the amount
she will receive depends on the market response to
her movie. What should she do?

Payouts and Probabilities
• Movie company Payouts
– Small box office - $200,000
– Medium box office - $1,000,000
– Large box office - $3,000,000
• TV Network Payout
– Flat rate - $900,000
• Probabilities
– P(Small Box Office) = 0.3
– P(Medium Box Office) = 0.6
– P(Large Box Office) = 0.1

Jenny Lind - Payoff Table
Decisions
States of Nature
Small Box
Office
Medium Box
Office
Large Box
Office
Sign with Movie
Company
$200,000 $1,000,000 $3,000,000
Sign with TV
Network
$900,000 $900,000 $900,000
Prior
Probabilities
0.3 0.6 0.1

Using Expected Return Criteria
EVmovie=0.3(200,000)+0.6(1,000,000)+0.1(3,000,000)
= $960,000 = EVUII or EVBest
EVtv =0.3(900,000)+0.6(900,000)+0.1(900,000)
= $900,000
Therefore, using this criteria, Jenny should select the movie
contract.

Decision Trees
• Three types of “nodes”
– Decision nodes - represented by squares ( )
– Chance nodes - represented by circles (Ο)
– Terminal nodes - represented by triangles (optional)
• Solving the tree involves pruning all but the best
decisions at decision nodes, and finding expected values
of all possible states of nature at chance nodes
• Create the tree from left to right
• Solve the tree from right to left

Example Decision Tree
Decision
node
Chance
node Event 1
Event 2
Event 3

Jenny Lind Decision Tree
Small Box Office
Medium Box Office
Large Box Office
Small Box Office
Medium Box Office
Large Box Office
Sign with Movie Co.
Sign with TV Network
$200,000
$1,000,000
$3,000,000
$900,000
$900,000
$900,000

Jenny Lind Decision Tree
Small Box Office
Medium Box Office
Large Box Office
Small Box Office
Medium Box Office
Large Box Office
Sign with Movie Co.
$200,000
$1,000,000
$3,000,000
$900,000
$900,000
$900,000
.3
.6
.1
.3
.6
.1
ER
?
ER
?
ER
?

Jenny Lind Decision Tree - Solved
Small Box Office
Medium Box Office
Large Box Office
Small Box Office
Medium Box Office
Large Box Office
Sign with Movie Co.
$200,000
$1,000,000
$3,000,000
$900,000
$900,000
$900,000
.3
.6
.1
.3
.6
.1
ER
900,000
ER
960,000
ER
960,000

Evaluation Metrics
Predicted as healthy Predicted as unhealthy
Actual healthy tp fn
Actual not healthy fp
tn

Cross-validation
• Correctly Classified Instances 143 95.3%
• Incorrectly Classified Instances 7 4.67 %
• Default 10-fold cross validation i.e.
– Split data into 10 equal sized pieces
– Train on 9 pieces and test on remainder
– Do for all possibilities and average

A Novel Approach for Breast Cancer
Detection using Data Mining Techniques

Abstract
 The aim of this paper is to investigate the
performance of different classification techniques.
 Aim is developing accurate prediction models for
breast cancer using data mining techniques
 Comparing three classification techniques in Weka
software and comparison results.
 Sequential Minimal Optimization (SMO) has
higher prediction accuracy than IBK and BF Tree
methods.

Introduction
 Breast cancer is on the rise across developing nations
 due to the increase in life expectancy and lifestyle
changes such as women having fewer children.
 Benign tumors:
• Are usually not harmful
• Rarely invade the tissues around them
• Don‘t spread to other parts of the body
• Can be removed and usually don‘t grow back
 Malignant tumors:
• May be a threat to life
• Can invade nearby organs and tissues (such as the
chest wall)
• Can spread to other parts of the body
• Often can be removed but sometimes grow back

Risk factors
 Gender
 Age
 Genetic risk factors
 Family history
 Personal history of breast cancer
 Race : white or black
 Dense breast tissue :denser breast tissue have a
higher risk
 Certain benign (not cancer) breast problems
 Lobular carcinoma in situ
 Menstrual periods

Risk factors
 Breast radiation early in life
 Treatment with DES : the drug DES (diethylstilbestrol)
during pregnancy
 Not having children or having them later in life
 Certain kinds of birth control
 Using hormone therapy after menopause
 Not breastfeeding
 Alcohol
 Being overweight or obese

BACKGROUND
 Bittern et al. used artificial neural network to
predict the survivability for breast cancer
patients. They tested their approach on a limited
data set, but their results show a good
agreement with actual survival Traditional
segmentation
 Vikas Chaurasia et al. used Representive Tree, RBF
Network and Simple Logistic to predict the survivability
for breast cancer patients.
 Liu Ya-Qin‘s experimented on breast cancer data using
C5 algorithm with bagging to predict breast cancer
survivability.

BACKGROUND
 Bellaachi et al. used naive bayes, decision
tree and back-propagation neural network to
predict the survivability in breast cancer
patients. Although they reached good results
(about 90% accuracy), their results were not
significant due to the fact that they divided the
data set to two groups; one for the patients
who survived more than 5 years and the other
for those patients who died before 5 years.
 Vikas Chaurasia et al. used Naive Bayes, J48
Decision Tree to predict the survivability for
Heart Diseases patients.

BACKGROUND
 Vikas Chaurasia et al. used CART (Classification
and Regression Tree), ID3 (Iterative Dichotomized
3) and decision table (DT) to predict the
survivability for Heart Diseases patients.
 Pan wen conducted experiments on ECG data to
identify abnormal high frequency
electrocardiograph using decision tree algorithm
C4.5.
 Dong-Sheng Cao‘s proposed a new decision tree
based ensemble method combined with feature
selection method backward elimination strategy to
find the structure activity relationships in the area
of chemo metrics related to pharmaceutical
industry.81 5/7/2014AAST-Comp eng

BACKGROUND
 Dr. S.Vijayarani et al., analyses the
performance of different classification function
techniques in data mining for predicting the
heart disease from the heart disease dataset.
The classification function algorithms is used
and tested in this work. The performance
factors used for analyzing the efficiency of
algorithms are clustering accuracy and error
rate. The result illustrates shows logistics
classification function efficiency is better than
multilayer perception and sequential minimal
optimization.82 5/7/2014AAST-Comp eng

BACKGROUND
 Kaewchinporn C‘s presented a new
classification algorithm TBWC combination of
decision tree with bagging and clustering.
This algorithm is experimented on two
medical datasets: cardiocography1,
cardiocography2 and other datasets not
related to medical domain.
 BS Harish et al., presented various text
representation schemes and compared
different classifiers used to classify text
documents to the predefined classes. The
existing methods are compared and
contrasted based on various parameters83 5/7/2014AAST-Comp eng

BREAST-CANCER-WISCONSIN
DATA SET SUMMARY

BREAST-CANCER-WISCONSIN DATA SET SUMMARY
 the UC Irvine machine learning repository
 Data from University of Wisconsin Hospital, Madison,
collected by dr. W.H. Wolberg.
 2 classes (malignant and benign), and 9 integer-
valued attributes
 breast-cancer-Wisconsin having 699 instances
 We removed the 16 instances with missing values
from the dataset to construct a new dataset with 683
instances
 Class distribution: Benign: 458 (65.5%) Malignant:
241 (34.5%)
 Note :2 malignant and 14 benign excluded hence
percentage is wrong and the right one is :
 benign 444 (65%) and malignant 239 (35%)

Attribute Domain
Sample Code Number Id Number
Clump Thickness 1 - 10
Uniformity Of Cell Size 1 - 10
Uniformity Of Cell Shape 1 - 10
Marginal Adhesion 1 - 10
Single Epithelial Cell Size 1 - 10
Bare Nuclei 1 - 10
Bland Chromatin 1 - 10
Normal Nucleoli 1 - 10
Mitoses 1 - 10
Class 2 For Benign
4 For Malignant

EVALUATION METHODS
 We have used the Weka (Waikato Environment for
Knowledge Analysis). version 3.6.9
 WEKA is a collection of machine learning algorithms
for data mining tasks.
 The algorithms can either be applied directly to a
dataset or called from your own Java code.
 WEKA contains tools for data preprocessing,
classification, regression, clustering, association
rules, visualization and feature selection.
 It is also well suited for developing new machine
learning schemes.
 WEKA is open source software issued under the
GNU General Public License

EXPERIMENTAL RESULTS

importance of the input variables
Domain 1 2 3 4 5 6 7 8 9 10 Sum
Clump Thickness 139 50 104 79 128 33 23 44 14 69 683
Uniformity of
Cell Size
373 45 52 38 30 25 19 28 6 67 683
Uniformity of
Cell Shape
346 58 53 43 32 29 30 27 7 58 683
Marginal
Adhesion
393 58 58 33 23 21 13 25 4 55 683
Single Epithelial
Cell Size
44 376 71 48 39 40 11 21 2 31 683
Bare Nuclei 402 30 28 19 30 4 8 21 9 132 683
Bare Nuclei 150 160 161 39 34 9 71 28 11 20 683
Normal Nucleoli 432 36 42 18 19 22 16 23 15 60 683
Mitoses 563 35 33 12 6 3 9 8 0 14 683
Sum 2843 850 605 333 346 192 207 233 77 516

Evaluation Criteria Classifiers
BF TREE IBK SMO
Timing To Build Model (In
Sec)
0.97 0.02 0.33
Correctly Classified
Instances
652 655 657
Incorrectly Classified
Instances
31 28 26
Accuracy (%) 95.46% 95.90% 96.19%

 The sensitivity or the true positive rate (TPR) is
defined by TP / (TP + FN)
 the specificity or the true negative rate (TNR) is
defined by TN / (TN + FP)
 the accuracy is defined by (TP + TN) / (TP + FP + TN
+ FN).
 True positive (TP) = number of positive samples
correctly predicted.
 False negative (FN) = number of positive samples
wrongly predicted.
 False positive (FP) = number of negative samples
wrongly predicted as positive.
 True negative (TN) = number of negative samples
correctly predicted

Classifier TP FP Precision Recall Class
BF Tree
0.971 0.075 0.96 0.971 Benign
0.925 0.029 0.944 0.925 Malignant
IBK
0.98 0.079 0.958 0.98 Benign
0.921 0.02 0.961 0.921 Malignant
SMO
0.971 0.054 0.971 0.971 Benign
0.946 0.029 0.946 0.946 Malignant

Classifier Benign Malignant Class
BF Tree
431 13 Benign
18 221 Malignant
IBK
435 9 Benign
19 220 Malignant
SMO
431 13 Benign
13 226 Malignant

importance of the input variables
variable
Chi-
squared
Info Gain
Gain
Ratio
Average
Rank
IMPORTANCE
Clump
Thickness
378.08158 0.464 0.152 126.232526 8
Uniformity of
Cell Size
539.79308 0.702 0.3 180.265026 1
Uniformity of
Cell Shape
523.07097 0.677 0.272 174.673323 2
Marginal
Adhesion
390.0595 0.464 0.21 130.2445 7
Single Epithelial
Cell Size
447.86118 0.534 0.233 149.542726 5
Bare Nuclei 489.00953 0.603 0.303 163.305176 3
Bland
Chromatin 453.20971 0.555 0.201 151.321903 4
Normal Nucleoli 416.63061 0.487 0.237 139.118203 6
Mitoses 191.9682 0.212 0.212 64.122733 9

CONCLUSION.
 the accuracy of classification techniques is evaluated
based on the selected classifier algorithm.
 we used three popular data mining methods:
Sequential Minimal Optimization (SMO), IBK, BF
Tree.
 The performance of SMO shows the high level
compare with other classifiers.
 most important attributes for breast cancer survivals
are Uniformity of Cell Size.

Future work
 using updated version of weka
 Using another data mining tool
 Using alternative algorithms and techniques

Notes on paper
 Spelling mistakes
 No point of contact (e - mail)
 Wrong percentage calculation
 Copying from old papers
 Charts not clear
 No contributions

comparison
 Breast Cancer Diagnosis on Three Different
Datasets Using Multi-Classifiers written
 International Journal of Computer and Information
Technology (2277 – 0764) Volume 01– Issue 01,
September 2012
 Paper introduced more advanced idea and make a
fusion between classifiers

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A Novel Approach for Breast Cancer Detection using Data Mining Techniques

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