Hybrid Adaptive Neuro-Fuzzy Inference System for Diagnosing the Liver Disorders
This work addresses liver disease diagnosis for medical applications, but it is incremental as it combines existing methods (ANFIS and PSO) without introducing a fundamentally new approach.
The authors tackled liver disorder diagnosis by proposing a hybrid ANFIS-PSO method, achieving improved performance over traditional FIS and ANFIS without optimization, with specific metrics like classification accuracy, sensitivity, and specificity evaluated on a dataset of 354 samples and 7 attributes.
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.