In developed and developing countries, breast cancer is one of the leading forms of cancer affecting women alike. As a consequence of growing lifeexpectancy, increasing urbanization and embracing Western lifestyles, ...In developed and developing countries, breast cancer is one of the leading forms of cancer affecting women alike. As a consequence of growing lifeexpectancy, increasing urbanization and embracing Western lifestyles, the highprevalence of this cancer is noted in the developed world. This paper aims todevelop a novel model that diagnoses Breast Cancer by using heterogeneous datasets. The model can work as a strong decision support system to help doctors tomake the right decision in diagnosing breast cancer patients. The proposed modelis based on three datasets to develop three sub-models. Each sub-model worksindependently. The final diagnosis decision is taken by the three sub-models independently. The power of the model comes from the diversity checks of patientsand this reduces the risk of wrong diagnosing. The model has been developedby conducting intensive experiments. Several classification algorithms were usedto select the best one in each sub-model. As the final results, the sub-modelaccuracies were 72%, 74% and 97%.展开更多
基金funding this work under grant number*(RGP.1/172/42)*,Received by Majdy M Eltahir.www.kku.edu.sa.
文摘In developed and developing countries, breast cancer is one of the leading forms of cancer affecting women alike. As a consequence of growing lifeexpectancy, increasing urbanization and embracing Western lifestyles, the highprevalence of this cancer is noted in the developed world. This paper aims todevelop a novel model that diagnoses Breast Cancer by using heterogeneous datasets. The model can work as a strong decision support system to help doctors tomake the right decision in diagnosing breast cancer patients. The proposed modelis based on three datasets to develop three sub-models. Each sub-model worksindependently. The final diagnosis decision is taken by the three sub-models independently. The power of the model comes from the diversity checks of patientsand this reduces the risk of wrong diagnosing. The model has been developedby conducting intensive experiments. Several classification algorithms were usedto select the best one in each sub-model. As the final results, the sub-modelaccuracies were 72%, 74% and 97%.
