A comparison of classification methods as diagnostic system: A case study on skin lesions. (December 2016) - Odeh, Suhail M.; Baareh, Abdel Karim Mohamed
Abstract: Background and objective: Numerous classification methods are currently available, but most of them were performed on different datasets. In this paper, different classification techniques were used for a diagnostic system on different skin lesions for the same data, which gives consistency for the data to have more accurate and better results. Methods: Four classification methods were proposed, a classical method based on K-Nearest Neighbor with Sequential Scanning selection technique for feature selection, a classical method with complex technique KNN with Genetic Algorithm, a complex method based on Artificial Neural Networks with Genetic Algorithm and an Adaptive Neuro-Fuzzy Inference System. Results: From the results obtained we can say that the performance of KNN with optimization of genetic algorithm for the feature selection was the best with an accuracy rate of 94%. The Adaptive Neuro-Fuzzy Inference System result was also good with an accuracy rate of 92%, and the other techniques' results were also satisfactory. Conclusion: The improvement on the performance of the classifier depends on the feature selection methods. In addition, the diagnosis system as a decision support tool could be used to increase the performance of human experts to make a correct decision.