A particle swarm optimization based classifier for liver disorders classification

A hybrid model is developed by integrating a case-based reasoning approach and a particle swarm optimization model for medical data classification. The data sets from UCI Machine Learning Repository; Liver Disorders Data Set is employed for benchmark test. Initially a case-based reasoning method is applied to preprocess the data set thus a weight vector for each feature is derived. A particle swarm optimization model is then applied to construct a decision-making system based on the selected features and diseases identified. The PSO algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions and then reducing the number of clusters into two. The average for liver disorders of CBRPSO is 78.18%. The proposed case-based particle swarm optimization model is able to produce more accurate and comprehensible results for medical experts in medical diagnosis..