Particle Swarm Optimization for Adjusting Fuzzy Parameters of Modified Ant-based Classification to Improve Medical Diagnosis

In the real world of medical diagnosis, interpretation and integration of the rule-based systems is significantly necessary. Fuzzy version of ant- classification (FAC) provides a framework of prominent achievement on fuzzy rule-based systems. This is caused by the nature of simplicity, accuracy and comprehensibility belonging to ant-based learning and fuzzy systems as well. However, local optimal traps is still a non-trivial problem during rules generating process. The Particle Swarm Optimization (PSO), a robust stochastic evolutionary algorithm based on the movement and intelligence of swarms indicates outstanding performance on a wide range of applications. This paper proposes PSO-MFAC, which utilizes particle swarm optimization algorithm to find the optimal fuzzy set parameters, associated with the modified fuzzy ant-based classification (MFAC). MFAC is a modified version of the traditional fuzzy ant-based classification in terms of attributes and training cases weighting. The proposed method, PSO-MFAC is tested with six critical medical diagnosis cases. The performance measure relates to accuracy rate as well as interpretability of the classification rules. The performance of the PSO-MFAC is compared with MFAC, FAC, and some other well-known, powerful classification approaches. The experimental results indicate the outperformance of PSO-MFAC over the others.