A high efficiency thyroid disorders prediction system with non-dominated sorting genetic algorithm NSGA-II as a feature selection algorithm
[ X ]
Tarih
2020
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
2020 International Conference for Emerging Technology, INCET 2020 -- 5 June 2020 through 7 June 2020 -- -- 162255
Anahtar Kelimeler
Cross Validation, Decision Tree, Non Dominated Sorting Genetic Algorithm (NSGA-II), Sequential Model, Support Vector Machine (SVM), Thyroid Disorders
Kaynak
2020 International Conference for Emerging Technology, INCET 2020
WoS Q Değeri
Scopus Q Değeri
N/A
