Machine learning models can predict the presence of variants in hemoglobin: artificial neural network-based recognition of human hemoglobin variants by HPLC
This article presents the use of machine learning techniques such as artificial neural networks, K-nearest neighbors (KNN), naive Bayes, and decision trees in the prediction of hemoglobin variants. To the best of our knowledge, this is the first study using machine learning models to predict suspici...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2022-11-01
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| Series: | Türk Biyokimya Dergisi |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/tjb-2022-0093 |
| _version_ | 1797848495852355584 |
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| author | Uçucu Süheyl Karabıyık Talha Azik Fatih Mehmet |
| author_facet | Uçucu Süheyl Karabıyık Talha Azik Fatih Mehmet |
| author_sort | Uçucu Süheyl |
| collection | DOAJ |
| description | This article presents the use of machine learning techniques such as artificial neural networks, K-nearest neighbors (KNN), naive Bayes, and decision trees in the prediction of hemoglobin variants. To the best of our knowledge, this is the first study using machine learning models to predict suspicious cases with HbS or HbD Los Angeles carriers state. |
| first_indexed | 2024-04-09T18:28:32Z |
| format | Article |
| id | doaj.art-3e39176ccefe4a28a5af870134d7b906 |
| institution | Directory Open Access Journal |
| issn | 1303-829X |
| language | English |
| last_indexed | 2024-04-09T18:28:32Z |
| publishDate | 2022-11-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Türk Biyokimya Dergisi |
| spelling | doaj.art-3e39176ccefe4a28a5af870134d7b9062023-04-11T17:42:47ZengDe GruyterTürk Biyokimya Dergisi1303-829X2022-11-0148151110.1515/tjb-2022-0093Machine learning models can predict the presence of variants in hemoglobin: artificial neural network-based recognition of human hemoglobin variants by HPLCUçucu Süheyl0Karabıyık Talha1Azik Fatih Mehmet2Department of Medical Biochemistry, Muğla Public Health Care Laboratory, Muğla, TurkiyeDepartment of Medical Biochemistry, Bursa City Hospital, Bursa, TurkiyeDepartment of Pediatric Hematology-Oncology, Faculty of Medicine, Muğla Sıtkı Koçman University, Muğla, TurkiyeThis article presents the use of machine learning techniques such as artificial neural networks, K-nearest neighbors (KNN), naive Bayes, and decision trees in the prediction of hemoglobin variants. To the best of our knowledge, this is the first study using machine learning models to predict suspicious cases with HbS or HbD Los Angeles carriers state.https://doi.org/10.1515/tjb-2022-0093artificial neural network (ann)deep learninghb d los angelesk-nearest neighbors (knn)sickle cell carrier |
| spellingShingle | Uçucu Süheyl Karabıyık Talha Azik Fatih Mehmet Machine learning models can predict the presence of variants in hemoglobin: artificial neural network-based recognition of human hemoglobin variants by HPLC Türk Biyokimya Dergisi artificial neural network (ann) deep learning hb d los angeles k-nearest neighbors (knn) sickle cell carrier |
| title | Machine learning models can predict the presence of variants in hemoglobin: artificial neural network-based recognition of human hemoglobin variants by HPLC |
| title_full | Machine learning models can predict the presence of variants in hemoglobin: artificial neural network-based recognition of human hemoglobin variants by HPLC |
| title_fullStr | Machine learning models can predict the presence of variants in hemoglobin: artificial neural network-based recognition of human hemoglobin variants by HPLC |
| title_full_unstemmed | Machine learning models can predict the presence of variants in hemoglobin: artificial neural network-based recognition of human hemoglobin variants by HPLC |
| title_short | Machine learning models can predict the presence of variants in hemoglobin: artificial neural network-based recognition of human hemoglobin variants by HPLC |
| title_sort | machine learning models can predict the presence of variants in hemoglobin artificial neural network based recognition of human hemoglobin variants by hplc |
| topic | artificial neural network (ann) deep learning hb d los angeles k-nearest neighbors (knn) sickle cell carrier |
| url | https://doi.org/10.1515/tjb-2022-0093 |
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