Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm

Molla, M. M. Imran and Jui, Julakha Jahan and Bari, Bifta Sama and Rashid, Mamunur and Hasan, Md Jahid (2020) Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm. In: The 11th National Technical Seminar on Unmanned System Technology 2019 (NUSYS’19) , 2-3 December 2019 , UMP, Gambang Campus, Kuantan, Malaysia. pp. 357-369., 666. ISBN 978-981-15-5281-6

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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, F-Score and Rand Index 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.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Fetal heart rate, Random Forest classifier, Cardiotocography.
Subjects: Q Science > QA Mathematics
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 20 Jan 2020 01:59
Last Modified: 22 Sep 2020 03:45
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