Adaptive Neuro Particle Swarm Optimization applied for diagnosing disorders
This work addresses the need for improved diagnostic tools in medical domains, but it appears incremental as it builds on existing optimization and fuzzy inference methods.
The paper tackles the problem of diagnosing disorders by proposing an Adaptive Neuro Particle Swarm Optimization (ANPSO) combined with a fuzzy inference system, resulting in a faster convergence rate and high accuracy for disease diagnosis, as demonstrated on the Bupa liver disorders dataset.
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.