An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods
This research proposes to use ensemble learning methods to diagnose and predict Turner syndrome using facial images. Turner syndrome, also known as congenital ovarian hypoplasia syndrome, is a common clinical chromosomal disorder. Without the aid of cytogenetic diagnostic results, the accuracy of di...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9266048/ |
| _version_ | 1819276465288511488 |
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| author | Qing Zhao Guohong Yao Faheem Akhtar Jianqiang Li Yan Pei |
| author_facet | Qing Zhao Guohong Yao Faheem Akhtar Jianqiang Li Yan Pei |
| author_sort | Qing Zhao |
| collection | DOAJ |
| description | This research proposes to use ensemble learning methods to diagnose and predict Turner syndrome using facial images. Turner syndrome, also known as congenital ovarian hypoplasia syndrome, is a common clinical chromosomal disorder. Without the aid of cytogenetic diagnostic results, the accuracy of diagnosis made by the paediatrician is unsatisfactory. Early diagnosis of the Turner syndrome requires the expertise of well-trained medical professionals, which may hinder early intervention due to a high potential cost. So far, most of the studies have reported the use of clinical chromosome detection to diagnose Turner syndrome. In this research, we are the first to use facial recognition technology to diagnose Turner syndrome using ensemble learning techniques. First, the features from each of the facial image are extracted by principal component analysis, kernel-based principal component analysis, and others. Second, we randomly selected samples and features to establish a basic learning model. Finally, we developed a combination of multiple basic learning models using majority voting and stacking for the facial image classification task. Experimental results show that the correct classification rate of the Turner syndrome detection was elevated up to 88.1%. The proposed method can be implemented to automatically diagnosis Turner syndrome patients that can facilitate clinicians during the prognosis process. |
| first_indexed | 2024-12-23T23:40:39Z |
| format | Article |
| id | doaj.art-d3567e1355cc4a169f893e4008487f30 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:40:39Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d3567e1355cc4a169f893e4008487f302022-12-21T17:25:41ZengIEEEIEEE Access2169-35362020-01-01822333522334510.1109/ACCESS.2020.30398679266048An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning MethodsQing Zhao0Guohong Yao1Faheem Akhtar2https://orcid.org/0000-0001-6755-1972Jianqiang Li3https://orcid.org/0000-0003-1995-9249Yan Pei4https://orcid.org/0000-0003-1545-9204Faculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaComputer Science Division, University of Aizu, Aizuwakamatsu, JapanThis research proposes to use ensemble learning methods to diagnose and predict Turner syndrome using facial images. Turner syndrome, also known as congenital ovarian hypoplasia syndrome, is a common clinical chromosomal disorder. Without the aid of cytogenetic diagnostic results, the accuracy of diagnosis made by the paediatrician is unsatisfactory. Early diagnosis of the Turner syndrome requires the expertise of well-trained medical professionals, which may hinder early intervention due to a high potential cost. So far, most of the studies have reported the use of clinical chromosome detection to diagnose Turner syndrome. In this research, we are the first to use facial recognition technology to diagnose Turner syndrome using ensemble learning techniques. First, the features from each of the facial image are extracted by principal component analysis, kernel-based principal component analysis, and others. Second, we randomly selected samples and features to establish a basic learning model. Finally, we developed a combination of multiple basic learning models using majority voting and stacking for the facial image classification task. Experimental results show that the correct classification rate of the Turner syndrome detection was elevated up to 88.1%. The proposed method can be implemented to automatically diagnosis Turner syndrome patients that can facilitate clinicians during the prognosis process.https://ieeexplore.ieee.org/document/9266048/Turner syndromeface recognitionensemble learningautomatic diagnosismachine learning |
| spellingShingle | Qing Zhao Guohong Yao Faheem Akhtar Jianqiang Li Yan Pei An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods IEEE Access Turner syndrome face recognition ensemble learning automatic diagnosis machine learning |
| title | An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods |
| title_full | An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods |
| title_fullStr | An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods |
| title_full_unstemmed | An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods |
| title_short | An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods |
| title_sort | automated approach to diagnose turner syndrome using ensemble learning methods |
| topic | Turner syndrome face recognition ensemble learning automatic diagnosis machine learning |
| url | https://ieeexplore.ieee.org/document/9266048/ |
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