A Review on Data Mining Techniques for Prediction of Breast Cancer Recurrence

International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
Volume- 9, Issue- 3 (June 2019)
www.ijemr.net https://doi.org/10.31033/ijemr.9.3.06
142 This work is licensed under Creative Commons Attribution 4.0 International License.
A Review on Data Mining Techniques for Prediction of Breast Cancer
Recurrence
R.S.PadmaPriya1
and P.Senthil Vadivu2
1
Assistant Professor, Department of Computer Technology, Dr.N.G.P Arts and Science College, Coimbatore, INDIA
2
Head of Department, Department of Computer Applications, Hindusthan College of Arts and Science, Coimbatore, INDIA
1
Corresponding Author: priypadma@gmail.com
ABSTRACT
The most common type of cancer in women
worldwide is the Breast Cancer. Breast cancer may be
detected early using Mammograms, probably before it's
spread. Recurrent breast cancer could occur months or years
after initial treatment. The cancer could return within the
same place because the original cancer (local recurrence), or it
may spread to different areas of your body (distant
recurrence). Early stage treatment is done not only to cure
breast cancer however additionally facilitate in preventing its
repetition/recurrence. Data mining algorithms provide
assistance in predicting the early-stage breast cancer that
continually has been difficult analysis drawback. The
projected analysis can establish the most effective algorithm
that predicts the recurrence of the breast cancer and improve
the accuracy the algorithms. Large information like Clump,
Classification, Association Rules, Prediction and Neural
Networks, Decision Trees can be analyzed using data mining
applications and techniques.
Keywords— Association Rules, Classification, Decision
Trees, Prediction, Recurrence
I. INTRODUCTION
Data mining is playing a great role in computing
applications in the domain area of medicine. Availability of
data mining applications and its techniques are shown in the
areas of healthcare administrations, patient care,
management, and intensive care system. One of the new
researches in data mining application involves analyzing
Breast cancer, which is one of the deadliest disease and
most common of all cancers in the leading cause of cancer
deaths in women worldwide. Classification
Algorithms plays an important role in Data Mining
research. Early stage treatment of this disease can give full
recovery from this disease. There are several data mining
applications and techniques which are used to analyze huge
data, those data mining techniques are Clustering,
Classification, Association Rules, Prediction and Neural
Networks Decisions Trees. Among these, some
classification algorithms such as Naïve Bayes (NB),
Support Vector Machine (SVM), Artificial Neural Network
(ANN), Decision Tree (C 5.0) and K nearest neighbor
(KNN) algorithm are used to obtain most accurate result.
Data mining is currently solving a lot of real world
problems. Because the main use of data mining technique is
to change raw data into more meaningful information. Both
male and female have chances of getting affected by breast
cancer. But from the world breast cancer statics the
occurrence of this disease is higher in female than males.
Recurrence of breast cancer is when the cancer is come
back after treated.
II. BREAST CANCER: AN OVERVIEW
The initial stage of Breast cancer begins when
cells in the breast begin to grow out of control. A tumor is
formed by these cells that can be seen on X-ray or it can be
felt as a lump. Breast cancer not only occurs entirely in
women, but men can also have the chances of affected by
breast cancer.
The common symptom of breast cancer is a new
swelling or accumulation. A hard mass that has irregular
edges without any pain is more likely to be cancer, but
breast cancers can be tender, soft, or rounded. They can
even be painful.
Breast cancer is a malignant or benign tumor, inside
breast, wherein cells divide and grow without control. As
there are a few risk factors which increase the like hood of a
woman developing breast cancer, the researchers tried to
find the exact reason behind breast cancer. The stage of a
cancer is one of the most important factors in selecting
treatment options, and it uses the Tumor, Nodes and
Metastasis (TNM) system. This is indicated as a form of
tumor from Stage 0 (the least advanced stage) to Stage IV
(the most advanced stage).
III. FACTORS FOR BREAST CANCER
IDENTIFICATION
Factors that can be used to identify breast cancer at
the earlier stage are as under:
1) Age
2) Alcohol Intake

3) Life Style
4) Heredity
5) Weight
6) Hereditary
7) Child Birth
Factors that can be used for the breast cancer
prediction are as follows:
Input Description
Clump
Thickness
Assesses if cells are mono- or multi-
layered.
Cell Size Evaluates the consistency in size of the
cells in the sample.
Cell Shape Estimates the equality of cell shapes and
identifies marginal variances.
Marginal
Adhesion
Quantifies how much cells on the outside
of the epithelial tend to stick together.
Single
Epithelial Cell
Size
Relates to cell uniformity, determines if
epithelial cells are significantly enlarged.
Bare Nuclei Calculates the proportion of the number
of cells not surrounded by cytoplasm to
those that are.
Bland
Chromatin
Rates the uniform "texture" of the nucleus
in a range from fine to coarse.
Normal
Nucleoli
Determines whether the nucleoli are
small and barely visible or larger, more
visible, and more plentiful.
Mitoses Describes the level of mitotic (cell
reproduction) activity.
IV. LITERATURE REVIEW
Shelly Gupta et al[1]
, observed that the accuracy
for the diagnosis analysis of various applied data mining
classification techniques is highly acceptable and can help
the medical professionals in decision making for early
diagnosis and to avoid biopsy. The problem is mainly
analyzed under ANNs that result in high accuracy when
compared to other classification techniques applied for the
same. The authors say that the best model can be obtained
after building several different types of models, or by trying
different technologies and algorithms.
Ahmad. LG et al[2]
studied the application of data
mining techniques to develop predictive models for breast
cancer recurrence in patients. They have used the machine
learning techniques, such as Decision Tree (C4.5), Support
Vector Machine (SVM), and Artificial Neural Network
(ANN) to develop the predictive models. The main goal of
this paper is to analyze the performance of these three
algorithms on the data through sensitivity, specificity, and
accuracy. The final outcome identified that SVM
classification model predicts breast cancer recurrence with
least error rate and highest accuracy. The 10-fold cross-
validation approach was used for measuring the unbiased
prediction accuracy of each model to obtain the results.
Chintan. Shah and Anjali. G. Jivani[3]
has
performed a comparative analysis between three
algorithms with help of WEKA (The Waikato Environment
for Knowledge Analysis), which is an open source
software. It contains different type’s data mining algorithms
namely Decision tree, Bayesian Network and K-Nearest
Neighbor algorithms. Here, for comparing the result, they
used the correctly classified instances, incorrectly classified
instances, time taken, kappa statistic, relative absolute error,
and root relative squared error as parameters.
B.Padmapriya and T.Velmurugan[4]
proposed the
classification algorithms ID3 and C4.5 to identify the
various categories of breast cancer. The behavior and
performance of both the algorithms were analyzed through
its experimental results. From their point of view, this
research work concluded that the performance of C4.5 is
better than the other algorithms.
Murari Kumar, Shivkumar Singh and Tomar
Bhupesh Gaur [5]
The authors have proposed a framework
with optimized mining that can be a better way in breast
cancer detection. There are several research directions in
this field and the scope is in the direction to fulfill it in the
earlier stage. The cancer symptoms are different and so it is
treated differently also o that the chances is more positive.
So the direction of research focuses on the identification of
cancer in the early stages.
B. R. A. Cirkovic, A. M. Cvetkovic, S. M.
Ninkovic, and D. Nenad [6]
described the practical
application of data mining methods for estimation of
survival rate and disease relapse for breast cancer patients.
A comparative study of prominent machine learning models
was carried out and according to the achieved results it was
concluded that the classifiers obviously learn some of the
concepts of breast cancer survivability and recurrence.
These algorithms were successfully applied to a novel
breast cancer data set of the Clinical Center of Kragujevac.
G. D. Rashmi et.al,[7]
used Naïve Bayes algorithm
for classification and prediction to analyze whether the
tumor is either benign or malignant. The dataset used is
taken from the Wisconsin University database. Data sets are
used to find the success rate and the error rate. The data sets
were chosen randomly. Finally Naïve Bayes Classification
gives 85-95% accuracy.
A. I. Pritom [8]
applied Naive Bayes, C4.5
Decision Tree and Support Vector Machine (SVM)
classification algorithms to calculate the prediction
accuracy of breast cancer recurrence. Data were collected
from Wisconsin dataset of UCI machine learning
Repository with total 35 attributes. The Accuracy of each
model can be improved by reducing some lower ranked

attributes with the help of an efficient feature selection
algorithm.
The research paper done by Joana Diz Goreti
Marreiros & Alberto Freitas[9]
presents new computer based
diagnosis system. By using this technique false positive
diagnosis test can be reduced. After data sets analyzed
Naive Bayes algorithm come with higher accuracy than
Random forest.
Two various data sets from Wisconsin breast cancer
have been evaluated by different data mining algorithms.
The outcome that Rotation Forest model shows the highest
classification accuracy (99.48 %) and when compared with
the previous works, the new approach and methodology
have come with highest performance and accuracy [10]
.
Jimin Guo, Benjamin C. M. Fung, Farkhund Iqbal
[11]
implemented decision tree algorithm with breast cancer
data sets collected from Leiden University Medical Center.
The data sets have 574 patients who have got surgery at that
hospital. So they generate the recurrence of breast cancer by
a decision tree algorithm within three years of initial
diagnosis. The classifier predicted 70% accuracy. For the
independent classifier of 65 patients the classifier exactly
predicts the recurrence of the disease in 55 patients. The
classifier also separates patient into two based on their
disease characteristic and their relevance of early relapse.
Rohit J Kate and Ramya Nadig[12]
used different
algorithm in order predict the breast cancer solvability. The
evaluation was done based the stage of the breast cancer.
Three machine learning algorithms were applied in order to
predict breast cancer survivability. These data sets were
evaluated by classification algorithms such as Naïve Bayes,
Logistic Regression and Decision Tree to predict breast
cancer survivability.
M.R.Mohebian[13]
et al., has defined breast cancer
recurrence prediction with clinicopathologic characteristics
of 579 breast cancer patients (recurrence prevalence of
19.3%) Datas were analyzed and discriminative features
were selected using statistical feature selection methods.
They were additionally distinguished by Particle Swarm
Optimization (PSO) as the inputs of the classification
system with ensemble learning (Bagged Decision Tree:
BDT). The PSO algorithm was used to identify the
combination of selected categorical features and also the
weight (importance) of the selected interval-measurement-
scale features. The performance of HPBCR (hybrid
predictor of breast cancer recurrence) was assessed as a
final outcome.
Akinsola Adeniyi F and et al, used Breast cancer
data set derived from Federal government hospital Lagos.
The authors used three supervised learning algorithms:
Multilayer Perceptron, Naïve Bayes and C 4.5 which were
executed using WEKA tool. Out of three algorithms the
authors concluded that C 4.5 produced more accuracy of
93.9854% with an execution time of 0.28 seconds.
Hamid Karim Khani Zan[16]
provided a study of
different technical and review papers on breast cancer
diagnosis and prognosis problems, outlined and resolved
the issues, algorithms, and techniques for the breast cancer
survivability prediction in SEER database.
Siyabend Turgut, Mustafa Datekin and Tolga
Ensari[17]
used microarray breast cancer data for
classification of the patients using machine learning
methods. First, 8 different machine learning algorithms
were applied to the data, with two different feature selection
methods. The methods applied are SVM, KNN, MLP,
Decision Trees, Random Forest, Logistic Regression,
Adaboost and Gradient Boosting Machines. After applying
the two different feature selection methods with the best 50
features are applied, SVM gave the best results. MLP is
applied using different number of layers and neurons to
examine the effect of the number of layers and neurons on
the classification accuracy. It is determined that the increase
in the number of layers sometimes decreased, sometimes
didn't change the accuracy.
V. PROPOSED RESEARCH
Breast cancer starts to grow in the human body
when cells in the breast are growing most in an unexpected
manner. After these cells grow, it can be seen by x- ray.
Basically, there are two types of breast cancer, cancer that
spread into another area and cancer that can’t spread into
another area. Among the world women breast cancer is the
first and the most leading of death of women and the
accurate diagnosis have lots of advantage to prevent and
detection of the disease. Data mining is a technique can
support doctors in the decision making process. As breast
cancer recurrence is high, good diagnosis is important. This
research is going to be implemented by different data
mining algorithms like Bayes net, support vector machine
and Decision tree (j48). So to get a more accurate value
about the recurrence of breast cancer we are going to use
data sets which were taken from the UCI machine learning
repository.
Table1. Review of Algorithm & Approaches
Title Methodology Results
Data Mining
Classification
Techniques Applied For
Breast Cancer
Diagnosis And
Prognosis [1]
Artificial Neural
Network
Bayesian Network
ANN has highest
Accuracy
Using Three Machine
Learning Techniques
for Predicting Breast
Cancer Recurrence [2]
Decision Tree,
SVM, ANN
SVM
classification
model predicts
breast cancer
recurrence with
least error rate

and highest
accuracy
Comparison of Data
Mining Classification
Algorithms for Breast
Cancer Prediction [3]
KNN, Decision
Tree, Naïve Bayes
Naïve Bayes
gives high
accuracy with
low execution
time.
A survey on breast
cancer analysis using
data mining
techniques[4]
Decision Tree C 4.5
, ID3
Performance of
C4.5 is better
than the other
algorithms
Mining based
Optimization for Breast
Cancer Analysis [5]
SVM, Neural
Network, Fuzzy
Theory, Genetic
Algorithm
In future a
framework with
optimized mining
can be a better
way in breast
cancer detection.
Prediction Models for
Estimation of Survival
Rate and Relapse for
Breast Cancer
Patients.[6]
SVM, Decision Tree The classifiers
obviously learn
some of the
concepts of
breast cancer
survivability and
recurrence
Analysis of Efficiency
of Classification and
Prediction Algorithms
(Naïve Bayes) for
Breast Cancer Data set
[7]
Naïve Bayes
classification
algorithm and Naïve
Bayes
prediction
algorithms
The Naïve Bayes
classification
algorithm has
highest accuracy
value
89 -95%
Predicting Breast
Cancer Recurrence
using effective
Classification and
Feature Selection
technique[8]
Decision tree C 4.5 ,
Support
vector machine,
Naïve Bayes
algorithms
Support vector
machine provide
better
performance
before and after
attribute
selection.
Applying Data Mining
Techniques to Improve
Breast Cancer
Diagnosis[9]
Naïve Bayes,
Random Forest
Naive Bayes
algorithm come
with higher
accuracy than
Random forest.
Breast cancer diagnosis
using GA feature
selection and Rotation
Forest[10]
Rotation forest
model
Rotation forest
shows highest
classification
accuracy
(99.48%)
Revealing determinant
factors for early breast
cancer recurrence by a
decision tree [11]
Decision tree The classifier
predicts for
whether a patient
developed early
disease
recurrence; and is
estimated to be
about 70%
accurate
Stage-specific
predictive models for
breast cancer
survivability[12]
Decision tree,(DT)
Naïve Bayes ,
Logistic regression
Decision Tree
Results in high
accuracy
Using three machine
learning techniques for
predicting breast cancer
recurrence [9]
Decision tree,
Artificial neural
network,
Support vector
machine
DT =0.936,
ANN =0.947,
SVM = 0.957. so
that SVM have
higher accuracy
than other
algorithms
Hybrid computer aided
diagnosis system for
prediction of breast
cancer recurrence using
optimized ensemble
learning [13]
SVM
Decision tree
multilayer
perception (MLP)
Support vector
machine give a
more accurate
value
Comparison of Data
Mining Classification
Algorithms for Breast
Cancer Prediction [11]
K Nearest Neighbor
algorithm
Decision Tree, and
Naïve Bayes
algorithm
Better
performance was
provided by
naïve Bayes
algorithm 95.9%
VI. CONCLUSION
As discussed in this survey breast cancer
recurrence is the most challenge of researchers for a lot of
years. The actual cause of breast cancer is unknown, but
early treatment may be a better way for prevention and
detection of breast cancer. In the above survey/review, it is
concluded that the data mining algorithms like Decision
Tree, Support Vector Machine, and Naïve Bayes are giving
more accurate results.
REFERENCES
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