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

Mohamed Alhag Alobed; Namareg Mohamed Ibrahim Ahmed

doi:doi:10.11648/j.ajdmkd.20230802.11

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

Mohamed Alhag Alobed, Namareg Mohamed Ibrahim Ahmed

Published in American Journal of Data Mining and Knowledge Discovery (Volume 8, Issue 2)

Received: 6 September 2023 Accepted: 13 October 2023 Published: 24 November 2023

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Abstract

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%.

Published in	American Journal of Data Mining and Knowledge Discovery (Volume 8, Issue 2)
DOI	10.11648/j.ajdmkd.20230802.11
Page(s)	18-22
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

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

References

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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

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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

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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

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@article{10.11648/j.ajdmkd.20230802.11,
  author = {Mohamed Alhag Alobed and Namareg Mohamed Ibrahim Ahmed},
  title = {An Efficient Prediction of Breast Cancer Diagnosis Using Data MiningTechnique in Tumor Therapy and Cancer Research Center Shendi},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {8},
  number = {2},
  pages = {18-22},
  doi = {10.11648/j.ajdmkd.20230802.11},
  url = {https://doi.org/10.11648/j.ajdmkd.20230802.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20230802.11},
  abstract = {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%.
},
 year = {2023}
}

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TY  - JOUR
T1  - An Efficient Prediction of Breast Cancer Diagnosis Using Data MiningTechnique in Tumor Therapy and Cancer Research Center Shendi
AU  - Mohamed Alhag Alobed
AU  - Namareg Mohamed Ibrahim Ahmed
Y1  - 2023/11/24
PY  - 2023
N1  - https://doi.org/10.11648/j.ajdmkd.20230802.11
DO  - 10.11648/j.ajdmkd.20230802.11
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 18
EP  - 22
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20230802.11
AB  - 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%.

VL  - 8
IS  - 2
ER  -

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Author Information

Mohamed Alhag Alobed

Cancer Research Unit, Tumor Therapy and Cancer Research Centre, Shendi University, Shendi Sudan

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Namareg Mohamed Ibrahim Ahmed

College of Computer Science, Sinnar University, Sinnar, Sudan

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Plain Text BibTeX RIS

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

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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

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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

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@article{10.11648/j.ajdmkd.20230802.11,
  author = {Mohamed Alhag Alobed and Namareg Mohamed Ibrahim Ahmed},
  title = {An Efficient Prediction of Breast Cancer Diagnosis Using Data MiningTechnique in Tumor Therapy and Cancer Research Center Shendi},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {8},
  number = {2},
  pages = {18-22},
  doi = {10.11648/j.ajdmkd.20230802.11},
  url = {https://doi.org/10.11648/j.ajdmkd.20230802.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20230802.11},
  abstract = {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%.
},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - An Efficient Prediction of Breast Cancer Diagnosis Using Data MiningTechnique in Tumor Therapy and Cancer Research Center Shendi
AU  - Mohamed Alhag Alobed
AU  - Namareg Mohamed Ibrahim Ahmed
Y1  - 2023/11/24
PY  - 2023
N1  - https://doi.org/10.11648/j.ajdmkd.20230802.11
DO  - 10.11648/j.ajdmkd.20230802.11
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 18
EP  - 22
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20230802.11
AB  - 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%.

VL  - 8
IS  - 2
ER  -

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