title: A comparison of classification methods as diagnostic system: A case study on skin lesions.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Computer and Information System Department, Bethlehem University, Bethlehem, Palestine. Electronic address: suhailodeh@gmail.com.
authors.forename: Suhail M
authors.initials: SM
authors.lastname: Odeh
authors.affiliation: Computers Science Department, Al-Balqa Applied University, Ajloun College, Jordan.
authors.forename: Abdel Karim Mohamed
authors.initials: AK
authors.lastname: Baareh
mesh_terms: D000465:Algorithms; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D012871:Skin Diseases / Q000145:classification* / Q000175:diagnosis*
keywords: Adaptive Neuro-Fuzzy Inference System (ANFIS); Artificial Neural Networks; Diagnosis system; Genetic algorithm; K nearest neighbour; Skin cancer