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A comparative study of the liver disorders prediction based on Neuro-Fuzzy and Metaheuristics approaches
| Content Provider | Semantic Scholar |
|---|---|
| Author | Bekaddour, Fatima Salim, Chikhi Okkacha, Bekaddour |
| Copyright Year | 2016 |
| Abstract | In this work, we propose the application of some well-known metaheuristics to enhance the medical classifier performance. IHBA (Improved Homogeneity-Based Algorithm), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) metaheuristics have been applied in conjunction with the neuro-fuzzy system to minimize the value of an objective function proposed in our previous work. We validate our computational results, based on the liver disorders dataset obtained from the UCI repository. Results show that the IHBA approach found the best performances. Both SA and PSO outperform the GA metaheuristic and the standard neuro-fuzzy model. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://acit2k.org/ACIT/images/stories/year2014/month1/proceeding/48_.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
