Improved hybrid models for automated classification of cardiotocogram data

Abstract

The major challenges in medical domain is the extraction of comprehensible knowledge from medical diagnosis such as Cardiotocography In this information era, the use of machine learning tools in medical diagnosis is increasing gradually This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases Cardiotocography consisting of fetal heart rate and tocographic measurements is used to evaluate fetal well being during the delivery Since 1970 many researchers have employed different methods to help the doctors to interpret the CTG trace pattern from the field of signal processing and computer programming They have supported doctors with interpretations in order to reach a satisfactory level of reliability so as to act as a decision support system in obstetrics More than 30 years after the introduction of antepartum Cardiotocography into clinical practice the predictive capacity of the method remains controversial In a review of lot of articles published on this subject it was found that its reported sensitivity newlinevaries between 2 and 100 and its specificity between 37 and 100 So in this work machine learning and datamining techniques are used for the classification of CTG data and propose new methods with improved classification accuracy newline newline