Breast cancer prediction based on backpropagation algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight women. Currently there are three techniques to diagnose breast cancer: mammography, FNA (Fine Needle Aspirate) and surgical biopsy. In this paper, we develop a system that can classify "Breast Cancer Disease" tumor using neural network with Feed-forward Backpropagation Algorithm to classify the tumor from a symptom that causes the breast cancer disease. The main aim of research is to develop more cost-effective and easy-to-use systems for supporting clinicians. For the breast cancer tumor diagnosis problem, experimental results show that the concise models extracted from the network achieve high accuracy rate of on the training data set and on the test data set. Breast cancer tumor database used for this purpose is from the University of Wisconsin (UCI) Machine Learning Repository.

Original languageEnglish
Title of host publicationProceeding, 2010 IEEE Student Conference on Research and Development - Engineering
Subtitle of host publicationInnovation and Beyond, SCOReD 2010
Pages164-168
Number of pages5
DOIs
Publication statusPublished - 01 Dec 2010
Event2010 8th IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010 - Kuala Lumpur, Malaysia
Duration: 13 Dec 201014 Dec 2010

Other

Other2010 8th IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010
CountryMalaysia
CityKuala Lumpur
Period13/12/1014/12/10

Fingerprint

Backpropagation algorithms
Tumors
cancer
Mammography
Biopsy
Needles
Learning systems
Disease
cause
Neural networks
neural network
Costs
death
costs
learning

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Education

Cite this

Mohd Azmi, M. S., & Che Cob, Z. (2010). Breast cancer prediction based on backpropagation algorithm. In Proceeding, 2010 IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010 (pp. 164-168). [5703994] https://doi.org/10.1109/SCORED.2010.5703994
Mohd Azmi, Muhammad Sufyian ; Che Cob, Zaihisma. / Breast cancer prediction based on backpropagation algorithm. Proceeding, 2010 IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010. 2010. pp. 164-168
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Mohd Azmi, MS & Che Cob, Z 2010, Breast cancer prediction based on backpropagation algorithm. in Proceeding, 2010 IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010., 5703994, pp. 164-168, 2010 8th IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010, Kuala Lumpur, Malaysia, 13/12/10. https://doi.org/10.1109/SCORED.2010.5703994

Breast cancer prediction based on backpropagation algorithm. / Mohd Azmi, Muhammad Sufyian; Che Cob, Zaihisma.

Proceeding, 2010 IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010. 2010. p. 164-168 5703994.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight women. Currently there are three techniques to diagnose breast cancer: mammography, FNA (Fine Needle Aspirate) and surgical biopsy. In this paper, we develop a system that can classify "Breast Cancer Disease" tumor using neural network with Feed-forward Backpropagation Algorithm to classify the tumor from a symptom that causes the breast cancer disease. The main aim of research is to develop more cost-effective and easy-to-use systems for supporting clinicians. For the breast cancer tumor diagnosis problem, experimental results show that the concise models extracted from the network achieve high accuracy rate of on the training data set and on the test data set. Breast cancer tumor database used for this purpose is from the University of Wisconsin (UCI) Machine Learning Repository.

AB - Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight women. Currently there are three techniques to diagnose breast cancer: mammography, FNA (Fine Needle Aspirate) and surgical biopsy. In this paper, we develop a system that can classify "Breast Cancer Disease" tumor using neural network with Feed-forward Backpropagation Algorithm to classify the tumor from a symptom that causes the breast cancer disease. The main aim of research is to develop more cost-effective and easy-to-use systems for supporting clinicians. For the breast cancer tumor diagnosis problem, experimental results show that the concise models extracted from the network achieve high accuracy rate of on the training data set and on the test data set. Breast cancer tumor database used for this purpose is from the University of Wisconsin (UCI) Machine Learning Repository.

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Mohd Azmi MS, Che Cob Z. Breast cancer prediction based on backpropagation algorithm. In Proceeding, 2010 IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010. 2010. p. 164-168. 5703994 https://doi.org/10.1109/SCORED.2010.5703994