BPSO_BreastCancer

#Feature optimization using Binary Particle Swarm Optimization

Breast cancer is currently one of the leading causes of cancer-related deaths among women around the world. Although the severity of the disease is undeniable, an efficient early diagnosis of the disease can lead to a much higher chance of survival for the patients. Effective clinical decision support systems could potentially be of very high utility for medical practitioners, in this regard. Here a binary Particle Swarm Optimization (BPSO) based feature selection approach is presented, which can be used to improve the performance of automatic breast cancer prediction. The key idea is to formulate the problem of feature selection in terms of a discrete optimization problem, with appropriate data-driven objective function.

#BINARY PARTICLE SWARM OPTIMIZATION PSO is an evolutionary computation technique proposed by Kennedy and Eberhart in 1995. PSO is motivated by social behaviours such as birds flocking and fish schooling. The underlying phenomenon of PSO is that knowledge is optimised by social interaction in the population where thinking is not only personal but also social.