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...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Singapore
2020
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| Online Access: | 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 |
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| 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 |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T12:40:15Z |
| format | Conference or Workshop Item |
| id | UMPir27518 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T12:40:15Z |
| publishDate | 2020 |
| publisher | Springer Singapore |
| record_format | dspace |
| spelling | UMPir275182020-09-22T03:45:59Z 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 |
| 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 |
| title | 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_short | Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm |
| title_sort | cardiotocogram data classification using random forest based machine learning algorithm |
| topic | QA Mathematics TK Electrical engineering. Electronics Nuclear engineering |
| 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 |
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