Abstract
The Cardiotocography is the most broadly utilized technique in obstetrics practice to monitor fetal health condition. The foremost motive of monitoring is to detect the fetal hypoxia at early stage. This modality is also widely used to record fetal heart rate and uterine activity. The exact analysis of cardiotocograms is critical for further treatment. In this manner, fetal state evaluation utilizing machine learning technique using cardiotocogram data has achieved significant attention. In this paper, we implement a model based CTG data classification system utilizing a supervised Random Forest (RF) which can classify the CTG data based on its training data. As per the showed up results, the overall performance of the supervised machine learning based classification approach provided significant performance. In this study, Precision, Recall and F-Score has been employed as the metric to evaluate the performance. It was found that, the RF based classifier could identify normal, suspicious and pathologic condition, from the nature of CTG data with 94.8% accuracy. We also highlight the major features based on Mean Decrease Accuracy and Mean Decrease Gini.
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The author would like to acknowledge the great supports by the Faculty of Electrical & Electronics Engineering and Universiti Malaysia Pahang, Malaysia.
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Imran Molla, M.M., Jui, J.J., Bari, B.S., Rashid, M., Hasan, M.J. (2021). Cardiotocogram Data Classification Using Random Forest Based Machine Learning Algorithm. In: Md Zain, Z., et al. Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019 . NUSYS 2019. Lecture Notes in Electrical Engineering, vol 666. Springer, Singapore. https://doi.org/10.1007/978-981-15-5281-6_25
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DOI: https://doi.org/10.1007/978-981-15-5281-6_25
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