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Biomaterials and Biomedical Engineering
  Volume 2, Number 3, September 2015, pages 173-183

Classification of cardiotocograms using random forest classifier and selection of important features from cardiotocogram signal
Muhammad Arif

    In obstetrics, cardiotocography is a procedure to record the fetal heartbeat and the uterine contractions usually during the last trimester of pregnancy. It helps to monitor patterns associated with the fetal activity and to detect the pathologies. In this paper, random forest classifier is used to classify normal, suspicious and pathological patterns based on the features extracted from the cardiotocograms. The results showed that random forest classifier can detect these classes successfully with overall classification accuracy of 93.6%. Moreover, important features are identified to reduce the feature space. It is found that using seven important features, similar classification accuracy can be achieved by random forest classifier (93.3%).
Key Words
    cardiotocography; fetal heart rate; random forest classifier; uterine contractions; biomedical data classification
Muhammad Arif: Department of Computer Science, College of Computer and Information systems, Umm-Alqura University, KSA

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