An Efficient Bio Inspired Bees Colony For Breast Cancer Prediction in Matlab

Abstract:

Breast cancer has increased mortality rate, one out of eight women have breast cancer. The breast cancer is viewed as the second most-common type of cancer. This is a big threat to women health and survival. One of the popular methods to predict breast cancer is the bio-inspired computing. Bio-inspired computing approaches are global optimization algorithms motivated by the natural behaviors of swarms such as ants, birds, fishes and bees. Artificial Bee Colony Algorithm (ABC) is a well-known bio-inspired algorithm, which is robust, easy to implement and has few setting parameters. However, one of ABC disadvantages is that of slow convergence due to poor exploration and exploitation processes. In this paper, we proposed a Global Guided Artificial Bee Colony (GGABC) algorithm. The proposed GGABC employs a new hybrid population based on metaheuristic approach to circumvent the deficiency of the standard ABC. The proposed algorithm was applied to predict patient's status of breast cancer. The approach was simulated by the foraging behavior of global best and guided honey bees. The simulation comparative analysis suggested that the proposed GGABC was found to converge to the optimal solution faster than the ABC, Guided ABC and Global ABC with an improved accuracy. The results of this research can provide critical information to health authorities to effectively manage the risk factors of the breast cancer in an early stage.