American Journal of Data Mining and Knowledge Discovery

Submit a Manuscript

Publishing with us to make your research visible to the widest possible audience.

Propose a Special Issue

Building a community of authors and readers to discuss the latest research and develop new ideas.

Research Article |

An Efficient Prediction of Breast Cancer Diagnosis Using Data MiningTechnique in Tumor Therapy and Cancer Research Center Shendi

Breast cancer is the disease that most common malignancy affects female. It has been considered as a second most common leading cause of cancer death among other type of cancer, specifically in developing countries. Most of the previous researches in mammogram images achieved low classification accuracy that because of either inaccurate features or improper classifier methods. Mammography is the most effective method for detection of early breast cancer to increase the survival rate. The aim of this research is to Enhancement of Mammogram Images Classification Accuracy Using Data mining technique (decision tree classifier) for medical datasets classification that can aid the physician in a mammogram image classification as benign or malignant. The study the study methodology focuses on six phases starting with image collection, pre-processing (cutting images of the area of interest), feature extraction, feature selection, classification, and ending with testing and evaluation. Experimental results using a mammogram analysis dataset from Tumor therapy and Cancer Research Center, Shendi Sudan, showed that this approach achieves an accuracy of 97.04%.

Breast Cancer, Mammogram Images, Data Mining Technique, Decision Tree

APA Style

Alhag Alobed, M., Mohamed Ibrahim Ahmed, N. (2023). An Efficient Prediction of Breast Cancer Diagnosis Using Data MiningTechnique in Tumor Therapy and Cancer Research Center Shendi. American Journal of Data Mining and Knowledge Discovery, 8(2), 18-22. https://doi.org/10.11648/j.ajdmkd.20230802.11

ACS Style

Alhag Alobed, M.; Mohamed Ibrahim Ahmed, N. An Efficient Prediction of Breast Cancer Diagnosis Using Data MiningTechnique in Tumor Therapy and Cancer Research Center Shendi. Am. J. Data Min. Knowl. Discov. 2023, 8(2), 18-22. doi: 10.11648/j.ajdmkd.20230802.11

AMA Style

Alhag Alobed M, Mohamed Ibrahim Ahmed N. An Efficient Prediction of Breast Cancer Diagnosis Using Data MiningTechnique in Tumor Therapy and Cancer Research Center Shendi. Am J Data Min Knowl Discov. 2023;8(2):18-22. doi: 10.11648/j.ajdmkd.20230802.11

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. BURKE, H. B., GOODMAN, P. H., ROSEN, D. B., HENSON, D. E., WEINSTEIN, J. N., HARRELL JR, F. E., MARKS, J. R., WINCHESTER, D. P. & BOSTWICK, D. G. 1997. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer, 79, 857-862.
2. SUGUNA, N. & THANUSHKODI, K. 2010. An improved k-nearest neighbor classification using genetic algorithm. International Journal of Computer Science Issues, 7, 18-21.
3. ZAıANE, O. R., ANTONIE, M.-L. & COMAN, A. 2002. Mammography classification by an association rule based classifier. MDM/KDD, 62-69.
4. MANDELBLATT, J. S., CRONIN, K., M. & PLEVRITIS, S. K. 2009. Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms. Annals of internal medicine, 151, 738-747.
5. SMITHA, P., SHAJI, L. & MINI, G. 2011. “A Review of Medical Image Classification Techniques.” International Conference on VLSI, Communication and Instrumentation. International Journal of Computer Application, 34-48.
6. ARNING, A., AGRAWAL, R. & RAGHAVAN, P. A Linear Method for Deviation Detection in Large Databases. KDD, 1996. 972-981.
7. RANGAYYAN, R. 2005. Chap. 7, Analysis of texture. Biomedical Image Analysis CRC Press LLC, Boca Raton, FL, 1277-1375.
8. LASHARI, S. A. & IBRAHIM, R. 2013. A framework for medical images classification using soft set. Procedia Technology, 11, 548-556.
9. ALI, S. & SMITH, K. A. 2006. On learning algorithm selection for classification. Applied Soft Computing, 6, 119-138.
10. HAN, J. & KAMBER, M. 2006. Data Mining: Concepts and Techniques, 2nd edition Morgan Kaufmann Publishers. San Francisco, CA, USA.
11. PAREEK, A. and S. M. ARORA, Breast cancer detection techniques using medical image processing. Breast cancer, 2017. 2 (3).
12. AARTHI, R., DIVYA, K., KOMALA, N. & KAVITHA, S. Application of Feature Extraction and clustering in mammogram classification using Support Vector Machine. 2011 Third International Conference on Advanced Computing, 2011. IEEE, 62-67.
13. PAREEK, A. & ARORA, S. M. 2017. Breast cancer detection techniques using medical image processing. Breast cancer, 2.
14. USHA, S. & ARUMUGAM, S. 2016. Calcification Classification in Mammograms Using Decision Trees. World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 9, 2127-2131.
15. IBRAHIM, A. O., AHMED, A., AZIZAH, A. H., LASHARI, S. A., ALOBEED, M. A., KASIM, S. & ISMAIL, M. A. 2018. An enhancement of multi classifiers voting method for mammogram image based on image histogram equalization. International Journal of Integrated Engineering.
16. MOHAMMED, N. S. M. 2021. Enhancing the Mammogram image Classification using Mutual Information Feature Selection. Sudan University of Science and Technology.
17. GANESAN, K., ACHARYA, U. R., CHUA, C. K., MIN, L. C. & ABRAHAM, T. K. 2014. Automated diagnosis of mammogram images of breast cancer using discrete wavelet transform and spherical wavelet transform features: a comparative study. Technology in cancer research & treatment, 13, 605-615.
18. EDDAOUDI, F., REGRAGUI, F., MAHMOUDI, A. & LAMOURI, N. 2011. Masses detection using SVM classifier based on textures analysis. Applied Mathematical Sciences, 5, 367-379.
19. KHARRAT, A., GASMI, K., MESSAOUD, M. B., BENAMRANE, N. 2010. A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine. Leonardo journal of sciences, 17, 71-82.
20. MOHAMED ALHAJ ALOBEED., ALI AHMED, ASHRAF IBRAHIM. Multi-classifier method based on voting technique for mammogram image classification, Journal of Software Engineering & Intelligent Systems issn 2518-8739. 31st December 2017, Volume 2, Issue 3.