PSO-ANN-Based Computer-Aided Diagnosis and Classification of Diabetes

Abstract

Feature selection (FS) is indeed a tough, challenging and demanding task due to the large exploration space. It moderates and lessens the number of features. It also eliminates insignificant, noisy, superfluous, repetitive and duplicate data and provides reasonably adequate classification accuracy. Present feature selection approaches do face the difficulties like stagnation in local optima, delayed convergence and high computational cost. In machine learning, particle swarm optimization (PSO) is an evolutionary computation procedure which is computationally less costly and can converge quicker than other existing approaches. PSO can be effectively used in various areas, like medical data processing, machine learning and pattern matching but its potential for feature selection is yet to be fully explored. PSO improves and optimizes a candidate solution iteratively with respect to a certain degree of quality. It provides a solution to the problem by having an inhabitant of swarm particles. By applying mathematical formulas, velocity and position of swarm particles are calculated and these particles are moved in the search space. The movement of individual swarm particle is inclined by its local finest known position and is also directed to the global finest known position in the exploration space. These positions are updated as improved positions, which are found by other particles. These improved positions are then used to move the swarm in the direction of the best solutions. The aim of the study is to inspect and improve the competence of PSO for feature selection. PSO functionalities are used to detect subset of features to accomplish improved classification performance than using entire features set.