Cardiotocography Data Set Classification with Extreme Learning Machine
Ayşenur Uzun E. ÇAPA KIZILTAŞ E. YILMAZ
AbstractThe purpose of the study is to efficient classification of Cardiotocography (CTG) Data Set from UCI Irvine Machine Learning Repository with Extreme Learning Machine (ELM) method. CTG Data Set has 2126 different fetal CTG signal recordings comprised of 23 real features. Data is two target class description that are based on fetal hearth rate and morphology pattern. The classification criteria based on morphology pattern (A-SUSP) is used in this study to serve better decision options to operators. Accuracy of ELM method will be compared with previous works in literature.