صورة الغلاف

Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm

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...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Molla, M. M. Imran, Jui, Julakha Jahan, Bari, Bifta Sama, Rashid, Mamunur, Hasan, Md Jahid
التنسيق: Conference or Workshop Item
اللغة:English
English
منشور في: Springer Singapore 2020
الموضوعات:
QA Mathematics
TK Electrical engineering. Electronics Nuclear engineering
الوصول للمادة أونلاين:http://umpir.ump.edu.my/id/eprint/27518/1/Cardiotocogram%20Data%20Classification%20using%20Random1.pdf
http://umpir.ump.edu.my/id/eprint/27518/2/Cardiotocogram%20Data%20Classification%20using%20Random.pdf
http://umpir.ump.edu.my/id/eprint/27518/
https://doi.org/10.1007/978-981-15-5281-6_25
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id my.ump.umpir.27518
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spelling my.ump.umpir.275182020-09-22T03:45:59Z http://umpir.ump.edu.my/id/eprint/27518/ Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm Molla, M. M. Imran Jui, Julakha Jahan Bari, Bifta Sama Rashid, Mamunur Hasan, Md Jahid QA Mathematics TK Electrical engineering. Electronics Nuclear engineering 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. Springer Singapore 2020-07-20 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27518/1/Cardiotocogram%20Data%20Classification%20using%20Random1.pdf pdf en http://umpir.ump.edu.my/id/eprint/27518/2/Cardiotocogram%20Data%20Classification%20using%20Random.pdf 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 https://doi.org/10.1007/978-981-15-5281-6_25
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA Mathematics
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle QA Mathematics
TK Electrical engineering. Electronics Nuclear engineering
Molla, M. M. Imran
Jui, Julakha Jahan
Bari, Bifta Sama
Rashid, Mamunur
Hasan, Md Jahid
Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm
description 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.
format Conference or Workshop Item
author Molla, M. M. Imran
Jui, Julakha Jahan
Bari, Bifta Sama
Rashid, Mamunur
Hasan, Md Jahid
author_facet Molla, M. M. Imran
Jui, Julakha Jahan
Bari, Bifta Sama
Rashid, Mamunur
Hasan, Md Jahid
author_sort Molla, M. M. Imran
title Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm
title_short Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm
title_full Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm
title_fullStr Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm
title_full_unstemmed Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm
title_sort cardiotocogram data classification using random forest based machine learning algorithm
publisher Springer Singapore
publishDate 2020
url http://umpir.ump.edu.my/id/eprint/27518/1/Cardiotocogram%20Data%20Classification%20using%20Random1.pdf
http://umpir.ump.edu.my/id/eprint/27518/2/Cardiotocogram%20Data%20Classification%20using%20Random.pdf
http://umpir.ump.edu.my/id/eprint/27518/
https://doi.org/10.1007/978-981-15-5281-6_25
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score 13.251813

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