  A new Adaptive Neuro Particle Swarm Optimization (ANPSO) combined with a
fuzzy inference system for diagnosing disorders is presented in this paper. The
main contributions of the novel proposed method can be a global search across
the whole search space with faster convergence rate. Moreover, it shows a
better exploration and exploitation by applying the adaptive control
parameters, automatic control of inertia weight and coefficient of personal and
social behaviours. Utilizing such attributes lead to a fast and smart diagnosis
mechanism which is able to diagnosis the diseases by the high accuracy. The
ANPSO is associated with tuning the characteristics of the inference system to
achieve the minimum diagnosis error as far as the optimized model is obtained.
As a case study, we use liver disorders dataset called Bupa. According to the
preliminary ramifications, the suggested adaptive PSO performance can overcome
the traditional inference system and combined with other optimization methods
substantially.
