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A high efficiency thyroid disorders prediction system with non-dominated sorting genetic algorithm NSGA-II as a feature selection algorithm
(Institute of Electrical and Electronics Engineers Inc., 2020) Kurnaz, Sefer; Mohammed, Mohammed Sami; Mohammed, Sahar Jasim
In 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.