An optimized K-Nearest Neighbor based breast cancer detection

Tsehay Admassu Assegie

Abstract


In this research, a grid search is employed to find the optimal parameter and an optimized K-Nearest Neighbor (KNN) based breast cancer detection model is proposed. The grid search is used to find the best combinations of parameters that could produce better breast cancer detection accuracy. Moreover, this study explored the effect of parameter tuning on the performance of KNN algorithm foe breast cancer detection. The findings of this research reveals that parameter tuning has a significant effect on the performance of the proposed model. The effect of parameter tuning on the performance of KNN algorithm is experimentally tested using Wisconsin breast cancer dataset collected from kaggle data repository. Finally, we have compared the performance of the KNN algorithm with the tuned hyper-parameter and with default hyper-parameter. The result analysis on the performance of the KNN algorithm on breast cancer detection on the test dataset reveals that the accuracy of the proposed optimized model is 94.35% and the performance of the KNN algorithm with the default hyper-parameter is 90.10%.

Keywords


breast cancer detection, KNN, optimized KNN, breast cancer, machine learning

Full Text:

PDF

References


P. Sahity Anaray Anan, Identification of Breast Cancer Using The Decision Tree Algorithm, proceedings of international conference on systems computation, automation and networking, IEEE, 2019.

R. Arulmozhiyal and K. Baskaran, "Implementation of a Fuzzy PI Controller for Speed Control of Induction Motors Using FPGA," Journal of Power Electronics, vol. 10, pp. 65-71, 2010.

Shahrbanoo Goli, Hossein Mahjub, Javad Faradma, Hoda Mashayekhi, Ali-Reza Soltanian, Survival Prediction and Feature Selection in Patients with Breast Cancer Using Support Vector Regression, Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2016.,

James Bergstra, Yoshua Bengio, Random Search for Hyper-Parameter Optimization, Journal of Machine Learning Research, 2012.

Tsehay Admassu Assegie, Pramod Sekharan Nair, The Performance Of Different Machine Learning Models On Diabetes Prediction, INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 01, JANUARY 2020.

Ashutosh Kumar Dubey, Umesh Gupta, Sonal Jain, Comparative Study of K-means and Fuzzy C-means Algorithms on The Breast Cancer Data, international journal on advanced science engineering and information technology, 2018.

Shubham Sharma, Archit Aggarwal, Tanupriya Choudhury, Breast Cancer Detection Using Machine Learning Algorithms, International Conference on Computational Techniques, Electronics and Mechanical System, 2018.

Mr.P .Sathy Anaranan, proceedings of international conference on systems computation automation and networking, IEEE, 2019.

Shofwatu Uyun, Lina Choridah, Feature Selection Mammogram based on Breast Cancer Mining, International Journal of Electrical and Computer Engineering (IJECE) Vol. 8, No. 1, February 2018.

Mohamed NEMISSI, Halima SALAH, Hamid SERIDI, Breast cancer diagnosis using an enhanced Extreme Learning Machine based-Neural Network, IEEE, 2019.

Tanaya Padhi, Praveen Kumar, Breast Cancer Analysis Using WEKA, IEEE, 2019.

Dr. S. N. Singh, Shivani Thakral, Using Data Mining Tools for Breast Cancer Prediction and Analysis, International Conference on Computing Communication and Automation, IEEE, 2018.

Amandeep Kaur, Prabhjeet Kaur, Breast Cancer Detection and Classification using Analysis and Gene-Back Proportional Neural Network Algorithm, International Journal of Innovative Technology and Exploring Engineering, 2019.

Tsehay Admassu Assegie, Sushma S J., Dr. Prasanna Kumar S C, Weighted Decision Tree Model for Breast Cancer Detection, Technology reports of Kansai university, Volume 62, Issue 03, January, 2020.

Seyyid Ahmed Medjahed, Tamazouzt Ait Saadi, Brwast Cancer Diagnosis by using k-Nearest Neighbor, International Journal of Computer Applications (0975 - 8887) Volume 62 - No. 2013.

Ahmed M, Abdel-Zaher, Ayman M. Eldeib, Breast cancer classification using deep belief networks, expert systems with application, Elsevier, 2016.

Tsehay Admassu Assegie, Sushma S. J, A Support Vector Machine and Decision Tree Based Breast Cancer Prediction, International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249 – 8958, Volume-9 Issue-3, February, 2020.

Adel S. Assiri, Saima Nazir, Sergio A. Velastin, Breast Tumor Classification Using an Ensemble Machine Learning Method, journal of imaging, 2020.

M.Chithra Devi, S.Audithan, Breast cancer using ensemble classification and extended weighted voting method International Journal of Advanced Research in Computer Science, 2017.

R.Chtihrakkannan, P.Kavitha, T.Mangayarkarasi, R.Karthikeyan, Breast Cancer Detection using Machine Learning, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-11, September 2019.

B.M.Gayathri, ,C.P.Sumathi, T.Santhanam, breast cancer diagnosis using machine learning algorithms: a survey, International Journal of Distributed and Parallel Systems (IJDPS) Vol.4, No.3, May 2013.

Y.Ireaneus Anna Rejani, early detection of breast cancer using svm classifier technique, international Journal on Computer Science and Engineering Vol.1(3), 2009.

Basa Varaj Highermath, Sc Prasann Kumar, International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), ISSN(P): 2249-6831; ISSN(E): 2249-7943, Vol. 5, Issue 1, Feb 2015.

P. Hamsagayathri, P. Sampath, Performance Analysis Of Breast Cancer Classification Using Decision Tree Classifiers, International Journal of Current Pharmaceutical Research, 2017.

Puneet Yadav, Rajat Varshney, Vishan Kumar Gupta, Diagnosis of Breast Cancer using Decision Tree Models and SVM, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056, Volume: 05 Issue: 03 | Mar-2018.

E. Venkatesan, T. Velmuruga, Performance Analysis of Decision Tree Algorithms for Breast Cancer Classification, Indian Journal of Science and Technology, Vol 8(29), IPL0625, November 2015.

Alaa. M. Elsayad, H. A. Elsalamony, Diagnosis of Breast Cancer using Decision Tree Models and SVM, International Journal of Computer Applications (0975 – 8887), Volume 83 – No 5, December 2013.

Mevlut Ture a, Fusun Tokatli, Imran Kurt, Using Kaplan–Meier analysis together with decision tree methods (C&RT, CHAID, QUEST, C4.5 and ID3) in determining recurrence-free survival of breast cancer patients, Expert Systems with Applications 36 (2009) 2017.

Yuehui Chen, Ajith Abraham, Bo Yang, Feature Selection and Classification using Flexible Neural Tree, Elsevier science, 2006.

Jehad Ali, Rehanullah Khan, Nasir Ahmad, Imran Maqsood, Random Forests and Decision Trees, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 5, No 3, September 2012.

Mr. Chintan Shah, Dr. Anjali G. Jivani, Comparison of Data Mining Classification Algorithm for Breast Cancer Prediction, research gate, 2015.




DOI: https://doi.org/10.18196/jrc.2363

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Journal of Robotics and Control (JRC)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

Journal of Robotics and Control (JRC)

P-ISSN: 2715-5056 || E-ISSN: 2715-5072
Organized by Peneliti Teknologi Teknik Indonesia
Published by Universitas Muhammadiyah Yogyakarta in collaboration with Peneliti Teknologi Teknik Indonesia, Indonesia and the Department of Electrical Engineering
Website: http://journal.umy.ac.id/index.php/jrc
Email: jrcofumy@gmail.com

Kuliah Teknik Elektro Terbaik