Open Access

ARTICLE

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

Majdy Mohamed Eltayeb Eltahir^1,*, Tarig Mohammed Ahmed^2,3

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

Received 23 August 2021; Accepted 20 October 2021; Issue published 08 February 2022

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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Keywords

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