Abstract
Improvement of Genetic Bee Algorithm to Select the Effective Features in Predicting Breast Cancer from Among Dietary Habits, Cultural Factors, Clinical Signs, and Laboratory Results
Highlights
The identification of factors influencing the incidence of breast cancer bears great importance
Medical records of 711 breast cancer patients were screened for 63 variables
Variables most affecting the incidence of breast cancer were identified using the genetic bee colony (GBC) algorithm and backup vector machine
Summary
The identification of factors influencing the incidence of breast cancer bears great importance. The wide range of symptoms of the disease makes the diagnosis difficult for doctors. Preventing breast cancer could be achieved via a knowledge of the factors affecting the incidence of the disease. The purpose of this paper was to identify variables related to dietary habits, cultural factors, and laboratory results that could contribute to the effective prediction of breast cancer. For this purpose, an optimal model based on genetic bee colony (GBC) algorithm was developed to increase machine learning accuracy
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