Abstract
One of the most common diseases among women is breast cancer, the early diagnosis of which is of paramount importance. Given the time-consuming nature of the diagnosis process of the disease, using new methods such as computer science is extremely important for early detection of the condition. Today, the main emphasis is on the science of data mining as one of the computer methods in the field of diagnosis. In the present study, we used data mining as a combination of feature selection method by Gray Wolf Optimization (GWO) and support vector machine (SVM), which is a new technique with high accuracy compared to other methods in this classification, to increase the accuracy of breast cancer diagnosis. The UCI dataset and functional parameters and various statistical criteria were applied to evaluate the proposed method and assess the validity of the results in MATLAB, respectively. Application of the proposed method increased the improvement of the evaluated criteria, which increased the accuracy of diagnosis by 27.68%, compared to former works in the field. As such, it could be concluded that the proposed method had a higher ability to diagnose breast cancer, compared to previous techniques.
Highlights
Breast cancer is the second leading cancer among women worldwide, the incidence of which has been reported to increases every year due to factors such as inheritance, lifestyle, and dietary habits [1]
The results revealed that the RepTree algorithm in the set of decision tree algorithms acted better in the breast cancer diagnosis and spent less time on creating the model
The results revealed that the support vector machine (SVM) along with two-stage algorithms could significantly improve the speed of prediction accuracy and reduce subjective classification error in cancer
Summary
Breast cancer is the second leading cancer among women worldwide, the incidence of which has been reported to increases every year due to factors such as inheritance, lifestyle, and dietary habits [1]. Industrialization and urbanization are among the other contributing factors to the increase incidence of breast cancer. The prevalence rate of breast cancer has been reported to be higher in high-income countries, while the incidence rate is on the rise in low-income countries as well, such as the regions in Africa, Asia, and Latin America [2]. In India, the mean age range of the high-risk population for breast cancer has been estimated at 43–46 years, and women aged 53–57 years are reported to be more susceptible to breast cancer [3].
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