ML BASED SVM TAKING RBF AS KERNEL FOR DETECTION OF BREAST CANCER

Abstract

This paper is a upward transaction done by the authors in the field of Artificial Intelligence and the Field called Machine Learning. The paper comprises of 5 Sections. In this paper we will observe an introductory note on the problem of Disease. We will have a discussion on Brest Cancer. We will observe an analysis in terms of graphs obtained using the tool called python. At a later part we will observe a classification report which is essentially a way to demonstrate the result obtained in the experiment made on the dataset available.

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JOURNAL OF EDUCATION: RABINDRA BHARATI UNIVERSITY
ISSN : 0972-7175
Vol.: XXV, No. :5(II), 2022
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ML BASED SVM TAKING RBF AS KERNEL FOR DETECTION OF BREAST CANCER
Dr. Nageshwar Dev Yadav
Product Lead Informatica R&D Lab Banglore, nagesh.yadav13@gmail.com
Vaibhav Kant Singh
Assistant Professor Department of CSE SoS E&T GGV (Central University) Bilaspur,
vibhu200427@gmail.com
Rahul Kumar Singh
Assistant Professor SoS in CS&IT Pt.RSU Raipur, rahulsingh.academic@gmail.com
Manish Sahu
Assistant Professor Department of SSPU Bhiali, manishsahu1@gmail.com
ABSTRACT
This paper is a upward transaction done by the authors in the field of Artificial Intelligence and the Field
called Machine Learning. The paper comprises of 5 Sections. In this paper we will observe an
introductory note on the problem of Disease. We will have a discussion on Brest Cancer. We will observe
an analysis in terms of graphs obtained using the tool called python. At a later part we will observe a
classification report which is essentially a way to demonstrate the result obtained in the experiment made
on the dataset available.
Keywords: Breast Cancer, SVM, Machine Learning, Python.
1. INTRODUCTION
In the past couple of year the world is stricken by the problem of COVID. The situation has put an alarm
on the all the countries around the globe to think over on the resources available to tackle the health care
problems. We are exposed that we are not ready to face a critical condition like this. In the current paper
we are highlighting a very alarming issue that the whole world if facing i.e. Breast Cancer. In [3] the
authors used a Machine Learning approach for diagnosis of Breast Cancer as benign and malignant.
2. LITERATURE REVIEW
In [1] the authors specified that Breast cancer is a type of cancer that is developed in the tissues of breast.
The symptoms of this type of cancer include some fluid coming out of the nipple, an inverted nipple, and
change in the shape of the breast, scaly or red patch in the skin and so on. There is a categorization found
in it on the basis of development. It may be ductal carcinomas or lobular carcinoma. In this case
sometimes both the breasts are removed so that the problem is overcome in high risk females. There are
various ways through which we can tackle this problem. The solutions include radiation therapy, targeted
therapy, chemotherapy and hormonal therapy. In countries like England and US the recovery rate is very
promising. In the current paper we will be visualizing a Machine Learning Technique to make a
prediction that whether a male/female based on the data collected on various parameters belong to benign
class or malignant class. Machine Learning is a buzzword in the current time[2]. There are various types
of models using which we can go through the above problem. In this paper we have used SVM approach.
Also we will look into various relationships obtained between the various parameters measured. In the
implementation of the above problem we have made a utilization of a very popular language called

JOURNAL OF EDUCATION: RABINDRA BHARATI UNIVERSITY
ISSN : 0972-7175
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Python [4]. We used it as it is open source. In [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32] the authors discuss their work in the field of Computer Science
and Engineering. In the work the authors made serious discussion on the ways how we can automate the
working or the operational environment to take advantage of the Computer System available with us. In
the work the authors made inroad on the several basic and advanced techniques used to handle a variety
of real time problems and had presented novel and impressive research work to made a utilization of
techniques to face a variety of problems.
3. METHODOLOGY
The following steps are involved in the Implementation of the above problem of identification of Cancer
as benign and malignant using Machine Learning approach i.e. SVM
1. Step1: Necessary imports
2. Step2: Loading of Data from the csv file obtained from Kaggle
3. Step3: Distribution of Classes
4. Step4: Selection of Columns that are unwanted
5. Step5: Removal of the Columns that are of no use
6. Step6: Dividing the Data into Train/Test dataset
7. Step7: Modeling
8. Step8: Evaluation of the Model on important Parameters
4. RESULTS
4.1 Kaggle Data-Set for Breast Cancer
In this paper the authors took the Kaggle Data Set meant specially for Breast Cancer. Authors observed
a total 33 attributes in dataset. The tuples present is 569. The total number of variation present in the
dataset is 18777. For Training the authors used 455 records and for testing the authors used 114 tuples.
Now in this section the authors present a number of graphs obtained between the parameters used in the
implementation.
Figure 1: radius_mean and smoothness_mean Scatter Plot

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Figure 2: perimeter_mean and concavity_mean Line plot
Figure 3: texture_mean and fractal_dimension_mean hist plot
Figure 4: concave points_se and radius_se bar plot

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Figure 5: radius_worst and smoothness_worst kde plot
Figure 6: concave points_worst and fractal_dimension_worst area plot
Figure 7: texture_worst and concavity_worst density plot

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Figure 8: compactness_mean and compactness_worst hexbin plot
5. CONCLUSION
In the paper the authors used the support vector machine as the ML technique to implement the
classification objective. In the model prepared the authors use SVC which is essentially the Support
Vector Classifier for the classification objective. In the implementation the authors use the kernel as
RBF. In Figure 9 you can observe the classification report
Figure 9: Python Code obtained Classification Report.
REFERENCES
1. Breast Cancer Treatment (PDQ) NCI. 23 May 2014.
2. T. Mitchel "What is Machine Learning?" Machine Learning. New York: McGraw
Hill. ISBN 0-07-042807-7. OCLC 36417892. www.ibm.com (1997).
3. E. Alpaydin Introduction to Machine Learning (Fourth ed.). MIT.vol. xix, 1–3, pp. 13–
18. ISBN 978-0262043793, (2020).
4. Guido van Rossum"An Introduction to Python for UNIX/C Programmers". Proceedings
of the NLUUG Najaarsconferentie (Dutch UNIX Users Group). CiteSeerX 10.1.1.38.2023 ,
(1993).
5. V. K. Singh,
“Proposing Solution to XOR problem using minimum configuration MLP,” Science Direct,
International Conference on Computational Modeling and Security (CMS 2016), Elsevier,
Procedia Computer Science, 85, pp. 263-270.

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6. V.K. Singh and
S. Pandey, “Minimum Configuration MLP for Solving XOR Problem,” Proceeding of 10th
INDIAcom, IEEE Conference ID:37465, 3rd International Conference on Computing for
Sustainable Global Development, BVICAM, pp. 168-173, New Delhi, India.
7. V.K. Singh,
“RSTDB & Cache Conscious Techniques for Frequent Pattern Mining,” Proceeding 4th
International Conference On Computer Applications In Electrical Engineering Recent Advances,
CERA-09, pp. 433-436, Indian Institute of Technology, Roorkee, 2010.
8. V.K. Singh,
“RSTDB a new candidate generation and test algorithm for frequent pattern mining,” Proceeding
International Conference on Advances in Communication Network and Computing, CNC-2010,
ACM DL Digital Library, ISBN: 978-0-7695-4209-6, pp. 416-418, IEEE Communication
Society, Washington DC, Calicut, Kerala, 4-5 Oct 2010.
9. V.K. Singh and
V.K. Singh, “ Minimizing Space Time Complexity by RSTDB a new method for Frequent Pattern
Mining,” Proceeding of the First International Conference on Human Computer Interaction,
Springer, New Delhi, pp. 361-371, Indian Institute of Information Technology, Allahabad, 20-23
Jan 2009.
10. V.K. Singh, “
Proposing a New ANN model for Solving XNOR problem,” IEEE Conference ID: 39669,
Proceeding IEEE 5th International Conference on System Modeling & Advancement in Research
Trends (SMART), ISBN: 978-1-5090-3543-4, pp. 32-36, Moradabad, India 25-27 Nov. 2016.
11. V.K. Singh,
“Designing Simulators for various VLSI Designs using the Proposed Artificial Neural Network
model TRIVENI,” IEEE Conference, Proceeding of IEEE International Conference on
Information, Communication, Instrumentation and Control (ICICIC), ISBN: 978-1-5090-6313-
0, pp. 1-6, Indore, India, 17-19 Aug 2017.
12. V.K. Singh and
A.K. Singh, “Dual Level Digital Watermarking for Images,” Proceeding of American Institute of
Physics (AIP) of International Conference on Methods and Models in Science and Technology
(ICM2ST-10), ISBN: 978-0-7354-0879-1, volume 1324, issue 01, pp. 284-287, 2010.
13. V.K. Singh, A.
Baghel, Dr. N.D. Yadav, M. Sahu and M. Jaiswal, “Machine Learning Approach to Detect Breast
Cancer,” Design Engineering (Toronto), Scopus Journal, Volume 2021, Issue 08, pp. 7054-7060,
ISSN: 0011-9342, 2021.
14. V.K. Singh, Dr.
N.D. Yadav and R.K. Singh”Diagnosis of Breast Cancer Using SVM taking polynomial as
Kernel,” Design Engineering (Toronto), Scopus Journal, Volume 2021, Issue 09, pp. 6589-6599,
ISSN: 0011-9342, 2021.
15. V.K. Singh,
“Proposing pattern growth methods for frequent pattern mining on account of its comparison
made with the candidate generation and test approach for a given data set,” Software Engineering,
Springer Singapore, pp. 203-209, 2019.

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16. V.K. Singh and S. Pandey,” Proposing an Ex-NOR Solution using ANN,” Proceeding
International Conference on Information, Communication and Computing Technology, JIMS,
New Delhi.
17. V.K. Singh, “Mathematical Explanation To Solution For Ex-NOR Problem Using
MLFFN,” International Journal of Information Sciences and Techniques,vol. 6,pp. 105-122,
2016.
18. V.K. Singh.,”Mathematical Analysis for Training ANNs Using Basic Learning
Algorithms,” Research Journal of Computer and Information Technology Sciences, 4(7),pp. 6-
13,2016.
19. V.K. Singh and V.K. Singh, “Vector Space Model : An Information Retrieval System,”
International Journal of Advanced Engineering Research and Studies, vol. 4(2), pp. 141-143.
20. V.K. Singh and V Shah, “Minimizing Space Time Complexity in Frequent Pattern Mining
by Reducing Database Scanning and Using Pattern Growth Method,” Chhattisgarh Journal of
Science & Technology ISSN: 0973-7219.
21. V.K. Singh and V.K. Singh, “The Huge Potential of Information Technology,”
Proceedings of National Convention on Global Leadership: Strategies and Challenges for Indian
Business, Feb pp.10-11.
22. V.K. Singh, “Analysis of Stability and Convergence on Perceptron Convergence
Algorithm,” pp.149-161, International Conference by JIMS Delhi.
23. V.K. Singh, “SVM using rbf as kernel for Diagnosis of Breast Cancer,” International
Conference on Innovative Research in Science, Management and Technology (ICIRSMT 2021),
Department of Computer Science and Application, Atal Bihari Vajpayee University, Bilaspur
(C.G.), India in association with American Institute of Management and Technology (AIMT),
USA, December 27-28 2021.
24. V.K. Singh, “Support Vector Machine using rbf, polynomial, linear and sigmoid as kernel
to detect Diabetes Cases and to make a Comparative Analysis of the Models,” International
Conference on Innovative Research in Science, Management and Technology (ICIRSMT 2021),
Department of Computer Science and Application, Atal Bihari Vajpayee University, Bilaspur
(C.G.), India in association with American Institute of Management and Technology (AIMT),
USA, December 27-28 2021.
25. V.K. Singh, “Colorization of old gray scale images and videos using deep learning,”
Published in The Journal of Oriental Research Madras, ISSN: 0022-3301, 2021.
26. V.K. Singh, “Dual Secured Data Transmission using Armstrong Number and Color
Coding,” Prestige e-Journal of Management and Research, Volume 3, Issue 1, ISSN: 2350-1316,
April 2016.
27. V.K. Singh, A. Baghel and S.K. Negi, “Finding New Framework for Resolving Problems
in Various Dimensions by the use of ES : An Efficient and Effective Computer Oriented Artificial
Intelligence Approach,” Volume 4, No. 11, ISSN(Paper): 2222-1727, ISSN(Online): 2222-2871,
2013.
28. Chandrashekhar, R. Chauhan and V.K. Singh,” Twitter Sentiment Analysis,” ISPEC 8TH
INTERNATIONAL CONFERENCE ON AGRICULTURAL, ANIMAL SCIENCE AND
RURAL DEVELOPMENT, BINGOL, TURKEY, DECEMBER 24-25, 2021.
29. P. Kumari, R. Gupta, S. Kumar and V.K. Singh,” ML Approach for Detection of Lung
Cancer,” ISPEC 8TH INTERNATIONAL CONFERENCE ON AGRICULTURAL, ANIMAL
SCIENCE AND RURAL DEVELOPMENT, BINGOL, TURKEY, DECEMBER 24-25, 2021.

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30. P. Sailokesh, S. Jupudi, I.K. Vamsi and V.K. Singh,” Automatic Number Plate
Recognition,” ISPEC 8TH INTERNATIONAL CONFERENCE ON AGRICULTURAL,
ANIMAL SCIENCE AND RURAL DEVELOPMENT, BINGOL, TURKEY, DECEMBER 24-
25, 2021.
31. Y.K. Reddy, K.M. Yadav and V.K. Singh,” Human Activity Recognition,” ISPEC 8TH
INTERNATIONAL CONFERENCE ON AGRICULTURAL, ANIMAL SCIENCE AND
RURAL DEVELOPMENT, BINGOL, TURKEY, DECEMBER 24-25, 2021.
32. R.N.R.K. Prasad, P.S.S.R Ram, S. Dinesh and V.K. Singh,” Text Summarization,” ISPEC
8TH INTERNATIONAL CONFERENCE ON AGRICULTURAL, ANIMAL SCIENCE AND
RURAL DEVELOPMENT, BINGOL, TURKEY, DECEMBER 24-25, 2021.