Performance Comparison of Artificial Neural Network techniques for Classification and Prediction of Breast Cancer

Hema Latha Loganathan


Data Mining is used to retrieve the information from the huge amount of data. Data mining is important field from all the fields for providing accurate prediction of diseases and deeper study of medical data. Data mining application involves analysing Breast cancer, which are the most dangerous disease cause of cancer deaths in women worldwide. Artificial Neural Network is used here to predict the breast cancer by analysing the nine technical indicators like clump thickness, uniformity of cell size, uniformity of cell shape, marginal adhesion, single epithelial cell size, bare nuclei, bland chromatin, normal nuclei and mitoses[3]. This study compares the four models namely Pattern Recognition Networks, Feed Forward Back Propagation Networks, Feed Forward Networks with no feedback, and Radial Basis Function Network to predict the breast cancer. The performance of all these models are examined from accuracy measures like Mean Square Error, Mean Absolute Error, Sum Square Error and Root Mean Square Error. From the examined result, the performance of Radial Basis Function network was considered as a best network for predicting the breast cancer.

Keywords: Data mining, Breast Cancer,  Feed Forward Back Propagation networks, Feed Forward networks, Pattern Recognition networks Radial Basis Function, Technical Indicators.

Full Text:



Souad Demigh (2015), “Data Mining for Breast Cancer Screening”- The 10th International Conference on Computer Science and Education, Cambridge University, UK.

Sumathi Mahadevan (2014), “Performance Comparision of Artificial Neural Network Techniques for Foreign Exchange Rate Forecasting” – International Journal of Applied Engineering Research.

Haifeng Wang, Sang Won Yoon (2015), “Breast Cancer Prediction using Data Mining Method” – Industrial and Systems Engineering Research Conference.

B.Padmapriya, T.Velmurugan (2014), “A survey on Breast Cancer Analysis Using Data Mining Techniques”-IEEE International Conference on Computational Inteeligence and Computinf Research.

Deepika Verma, Dr.Nidhi Mishra (2017), “Analysis and Prediction of Breast Cancer and Diabetes Disease Dataset using Data Mining Classification Techniques - International Conference on Intelligent Sustainable Systems.

A.Soltani Sarvestani, A.A. Safavi, N.M.Parandeh, M.Salehi(2010), “Predicting Breast Cancer Survivability Using Data Mining Techniques” - International Conference on Software Technology and Engineering.

Runjie Shen, Yuanyuan Yang, Fengfeng Shao(2014), “Intelligent Breast Cancer Prediction Model Using Data Mining Techniques” - Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics.

Veronica Burriel, Oscar Pastor, Maria Pena-Chilet, Maria T.Martinez, Gloria Ribas (2016) , “ Conceptual Schema on miRNA’s Expression using efficient information systems practices to manage and analyze data about miRNA expression studies in breast cancer.

Kehinde Williams, Jeremiah Ademola Balogun, Peter Adebayo Idowu (2015), “Breast Cancer Risk Prediction Using Data Mining Classification Techniques” -Society For Science and Education, UK (Volume 3).

Savita Kumari (2018), “Breast Cancer Classification Using Big Data Approach”- Indian Journal Of Research (Volume 7).

Tulay Yildirim (2003), “Breast CancerDiagnosis Using Statistical Neural Networks” - Turkish Symposium on Artificial Intelligence and Neural Network.

Shellu Gupta, Dharminder Kumar, Anand Sharma (2011),“Data Mining Classification Techniques Applied For Breast Cancer Diagnosis and Prognosis”-Indian Journal of Computer Science and Technology.

Autsuo Higa (2018), “Diagnosis of Breast Cancer Using Decision Tree and Artificial Neural Network Analysis” - International Journal of Conference Application Technology and Research.

Shelly Gupta, Dharminder Kumar, Anand Sharma (2011), “Data Mining Classification Techniques Applied for Breast Cancer Diagnosis and Prognosis”- Indian Journal of Computer Science and Engineering.

Abdelghani Bellaachia, Erhan Guven (2005), “Predicting Breast Cancer Survivability Using Data Mining Techniques” - Department of Computer Science, The George Washington University.

Ahmad LG, Eshlaghy AT, Poorebrahimi A, Ebrahimi M and Razavi AR (2012), “Using Three Machine LearningTechniques for Predicting Breast Cancer Recurrence” - Health and Medical Informatics (Volume 2).

S.Padma Priya, P.Sowmiya (2018), “Breast Cancer Prediction Using Data Mining Techniques” - International Journal of Research in Science and Engineering.

K.Sivami (2015), “Mining Big Data: Breast Cancer Prediction Using DT-SVM Hybrid Model” - International Journal of Scientific Engineering and Applied Science (Volume 1).

G.Ravikumar, Dr.G.A.Ramachandra, K.Nagamani (2013), “An Efficient Prediction of Breast Cancer Data Using Mining Techniques” - International Journal of Innovations in Engineering and Technology.

Stupid Mandal, Indrajit Banerjee (2015), “Cancer Classification Using Neural Networks” - International Journal of Emerging Engineering Research and Technology.



© International Journals of Advanced Research in Computer Science and Software Engineering (IJARCSSE)| All Rights Reserved | Powered by Advance Academic Publisher.