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Diagnosing Breast Cancer Accurately Based on Weighting of Heterogeneous Classification Sub-Models

Majdy Mohamed Eltayeb Eltahir1,*, Tarig Mohammed Ahmed2,3

1 Department of Information Systems, College of Science & Arts at Mahayil, King Khalid University, Muhayel Aseer, 62529, Kingdom of Saudi Arabia
2 Department of Information Technology, Faculty of Computing and Information Technology, King Abdul-Aziz University, Jeddah, 21589, Kingdom of Saudi Arabia
3 Department of Computer Sciences, Faculty of Mathematical Sciences and Informatics, University of Khartoum, Khartoum, 11115, Sudan

* Corresponding Author: Majdy Mohamed Eltayeb Eltahir. Email: meltahir@ kku.edu.sa

(This article belongs to the Special Issue: Recent Advancement in Information Technology in Healthcare and Patient Management)

Computer Systems Science and Engineering 2022, 42(3), 1257-1272. https://doi.org/10.32604/csse.2022.022942

Abstract

In developed and developing countries, breast cancer is one of the leading forms of cancer affecting women alike. As a consequence of growing life expectancy, increasing urbanization and embracing Western lifestyles, the high prevalence of this cancer is noted in the developed world. This paper aims to develop a novel model that diagnoses Breast Cancer by using heterogeneous datasets. The model can work as a strong decision support system to help doctors to make the right decision in diagnosing breast cancer patients. The proposed model is based on three datasets to develop three sub-models. Each sub-model works independently. The final diagnosis decision is taken by the three sub-models independently. The power of the model comes from the diversity checks of patients and this reduces the risk of wrong diagnosing. The model has been developed by conducting intensive experiments. Several classification algorithms were used to select the best one in each sub-model. As the final results, the sub-model accuracies were 72%, 74% and 97%.

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APA Style
Eltahir, M.M.E., Ahmed, T.M. (2022). Diagnosing breast cancer accurately based on weighting of heterogeneous classification sub-models. Computer Systems Science and Engineering, 42(3), 1257-1272. https://doi.org/10.32604/csse.2022.022942
Vancouver Style
Eltahir MME, Ahmed TM. Diagnosing breast cancer accurately based on weighting of heterogeneous classification sub-models. Comput Syst Sci Eng. 2022;42(3):1257-1272 https://doi.org/10.32604/csse.2022.022942
IEEE Style
M.M.E. Eltahir and T.M. Ahmed, "Diagnosing Breast Cancer Accurately Based on Weighting of Heterogeneous Classification Sub-Models," Comput. Syst. Sci. Eng., vol. 42, no. 3, pp. 1257-1272. 2022. https://doi.org/10.32604/csse.2022.022942



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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