Title :
A particle swarm optimization based classifier for liver disorders classification
Author :
Lin, Jyun Jie ; Chang, Pei-Chann
Author_Institution :
Dept. of Inf. Manage., Yuan Ze Univ., Chungli, Taiwan
Abstract :
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..
Keywords :
case-based reasoning; diseases; learning (artificial intelligence); liver; medical diagnostic computing; particle swarm optimisation; pattern classification; PSO algorithm; UCI Machine Learning Repository; case based reasoning; decision making system; liver disorders classification; liver disorders data set; medical data classification; medical diagnosis; particle swarm optimization based classifier; particle swarm optimization model; Accuracy; Classification algorithms; Computational modeling; Data models; Liver; Medical diagnostic imaging; Particle swarm optimization;
Conference_Titel :
Computational Problem-Solving (ICCP), 2010 International Conference on
Conference_Location :
Lijiang
Print_ISBN :
978-1-4244-8654-0