Gender Prediction Based On The Expiratory Flow Volume Curve

Cosgun S., ÖZBEK İ. Y.

23nd Signal Processing and Communications Applications Conference (SIU), Malatya, Turkey, 16 - 19 May 2015, pp.2118-2121 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2015.7130290
  • City: Malatya
  • Country: Turkey
  • Page Numbers: pp.2118-2121
  • Ataturk University Affiliated: Yes

Abstract

This study is performed estimated using the gender of the person is the expiration of the current-volume curve obtained from the test. Gender studies estimate is carried out using two different machine learning method. These methods Gaussian Mixture Model (GMM) and Support Vector Machines are (SVM). Gender prediction in both methods are performed using classification. The proposed methods have three main stages. These stages are feature extraction, training and gender of test person is detected. Performance evaluation is made according to the experimental results obtained. As a result of these studies, the gender prediction accuracy of 99.43 per cent are carried out.