Kurnaz, SeferMohammed, Mohammed SamiMohammed, Sahar Jasim2021-05-152021-05-1520209781728162218https://doi.org/10.1109/INCET49848.2020.9154189https://hdl.handle.net/20.500.12939/11032020 International Conference for Emerging Technology, INCET 2020 -- 5 June 2020 through 7 June 2020 -- -- 162255In spite of availability of patient's data in hospitals, health care institute and websites but still hard to collected especially for a risk disease like thyroid disorders. A new model by using Non Sorting Genetic Algorithm are selected for rows reductions and attributes selected with a three data mining techniques for a faster and accurate thyroid disorders detection. Two types of thyroid disorders with 4 different classes for each type are used for this design, in addition 500+972 are used with 29 attributes as training and testing data respectively with cross validation=5. Performances of this model are measured by using some parameter as accuracy , precision , etc. This model is studied for using all/some features with the proposed model and compare it with Sequential model. A scatter plot and area under curve are also presented in this work for training data to show the classes predication enhancement. © 2020 IEEE.eninfo:eu-repo/semantics/closedAccessCross ValidationDecision TreeNon Dominated Sorting Genetic Algorithm (NSGA-II)Sequential ModelSupport Vector Machine (SVM)Thyroid DisordersConference Object10.1109/INCET49848.2020.91541892-s2.0-85090570965N/A