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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/11425

Title: A Multigene Genetic Programming Model for Thyroid Disorder Detection
Authors: Ackora-Prah, Joseph
Oheneba-Osei, Fidelis Nyame
Andam, Perpetual Saah
Gyamfi, Daniel
Gyamerah, Samuel Asante
Keywords: Hyperthyroidism
Hypothyroidism
Multigene Symbolic Regression Genetic Programming technique
Issue Date: 2015
Publisher: Applied Mathematical Sciences
Citation: Applied Mathematical Sciences, Vol. 9, 2015, no. 135, 6707 - 6722; http://dx.doi.org/10.12988/ams.2015.59563
Abstract: Two common diseases of the thyroid gland, which releases thyroid hormones for regulating the rate of the body's metabolism, are hyper- thyroidism and hypothyroidism. Before a patient is classi ed as being normal or su ering from hyperthyroidism or hypothyroidism, there are a lot of information and tests conducted on the patient by existing models and these are costly in terms of time and money. We present the detec- tion of thyroid disorder based on attributes collected from patients. A mathematical model is developed using Multigene Symbolic Regression Genetic Programming technique. The results show that the model is good and is even able to reduce the number of attributes used to classify a patient as Normal, Hyperthyroidism and Hypothyroidism.
Description: An article published in Applied Mathematical Sciences, Vol. 9, 2015, no. 135, 6707 - 6722; http://dx.doi.org/10.12988/ams.2015.59563
URI: http://hdl.handle.net/123456789/11425
Appears in Collections:College of Science

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