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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Main Authors:	Molla, M. M. Imran, Jui, Julakha Jahan, Bari, Bifta Sama, Rashid, Mamunur, Hasan, Md Jahid
Format:	Conference or Workshop Item
Language:	English English
Published:	2019
Subjects:	QA Mathematics TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/27518/ 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

id	ump-27518
recordtype	eprints
spelling	ump-275182020-01-20T01:59:09Z 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. 2019 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 (2019) 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. 1-13.. (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
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
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/27518/ 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
first_indexed	2023-09-18T22:43:17Z
last_indexed	2023-09-18T22:43:17Z
_version_	1777417058110144512

Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm

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