Estimating the Risk of a Down's Syndrome Term Pregnancy Using Age and Serum Markers: Comparison of Logistic Regression, Quadratic Discriminant Analysis, and Support Vector Machines

dc.contributor.advisor	Sándor, Baran
dc.contributor.author	Ang'ang'o, Pauline
dc.contributor.department	DE--Természettudományi és Technológiai Kar--Matematikai Intézet
dc.date.accessioned	2024-06-11T07:39:50Z
dc.date.available	2024-06-11T07:39:50Z
dc.date.created	2024-04-25
dc.description.abstract	This thesis aims to enhance the precision and safety of Down's syndrome (DS) risk assessment by exploring innovative, non-invasive approaches in prenatal healthcare. Traditional invasive procedures, like amniocentesis, pose risks, motivating our investigation into alternative methods. We concentrate on Logistic Regression, Quadratic Discriminant Analysis (QDA), and Support Vector Machines (SVM), each offering unique strengths in risk estimation.
dc.description.course	Applied Mathematics
dc.description.degree	MSc/MA
dc.format.extent	61
dc.identifier.uri	https://hdl.handle.net/2437/371339
dc.language.iso	en
dc.rights.access	Hozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subject	Down's Syndrome
dc.subject	Logistic Regression
dc.subject	Support Vector Machines
dc.subject	Quadratic Discriminant Analysis
dc.subject.dspace	DEENK Témalista::Matematika	hu_HU
dc.title	Estimating the Risk of a Down's Syndrome Term Pregnancy Using Age and Serum Markers: Comparison of Logistic Regression, Quadratic Discriminant Analysis, and Support Vector Machines

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