Swarm Based-Artificial Neural System for Human Health Data Classification

Cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications. The accurate prediction in patients with early-stage cancer is of significant importance to reduce the mortality rate of those patients. Therefore, biologically inspired approaches that are motivated by the natural behaviors of swarms are used in this work. They are robust, easy to implement, and has few setting parameters. However, one disadvantage is that they are of slow convergence, which is due to the poor exploration and exploitation processes. To overcome this deficiency of the traditional algorithm, we propose the Global Guided Artificial Bee Colony (GGABC) and Hybrid Guided Artificial Bee Colony (HGABC) algorithms; which employ new hybrid population-based meta-heuristic approaches, simulated by the foraging behaviors of global best and guided honey bees. In this chapter, GGABC and HGABC algorithms are proposed in order to determine whether patients have cancer or not. The simulation results of the proposed approaches were compared with other algorithms including ABC, Guided ABC, and Global ABC. The classification results of cancer by GGABC and HGABC models are highly accurate in contrast to the results given by the benchmarked algorithms.

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