Prenatal risk assessment of trisomy 21 by probabilistic classifiers Olasiliksal siniflandiricilar ile doǧum öncesinde trizomi 21 risk hesaplamas

Uzun Ö., Kaya H., Gürgen F., Varol F. G.

2013 21st Signal Processing and Communications Applications Conference, SIU 2013, Haspolat, Türkiye, 24 - 26 Nisan 2013, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu.2013.6531604
  • Basıldığı Şehir: Haspolat
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: Bayesian networks, Classification, Down syndrome, Machine learning, Naive bayes, Probabilisitc classifiers, Trizomi21
  • Trakya Üniversitesi Adresli: Evet

Özet

This study proposes a probabilistic approach to evaluate prenatal risk of Down syndrome. In this study, we address the decision-making problem in diagnosing Down syndrome from the machine learning perspective aiming to decrease invasive tests. We employ Naive Bayes and Bayesian Networks classification algorithms as probabilistic methods. This probabilistic classification approach is one of the leading work in medical domain. We use George Washington University dataset in our study. We also benchmark our probabilistic classifiers with widely used non-probabilistic classifiers in machine learning literature. Finally the results of the experiments show that probabilistic classifiers enable acceptable prediction of Trisomy 21 case and the classification performance can be improved by using the proposed techniques in this study. © 2013 IEEE.