  In this study, a hybrid method based on an Adaptive Neuro-Fuzzy Inference
System (ANFIS) and Particle Swarm Optimization (PSO) for diagnosing Liver
disorders (ANFIS-PSO) is introduced. This smart diagnosis method deals with a
combination of making an inference system and optimization process which tries
to tune the hyper-parameters of ANFIS based on the data-set. The Liver diseases
characteristics are taken from the UCI Repository of Machine Learning
Databases. The number of these characteristic attributes are 7, and the sample
number is 354. The right diagnosis performance of the ANFIS-PSO intelligent
medical system for liver disease is evaluated by using classification accuracy,
sensitivity and specificity analysis, respectively. According to the
experimental results, the performance of ANFIS-PSO can be more considerable
than traditional FIS and ANFIS without optimization phase.
