An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA
Background: Since screening of α-thalassemia carriers by low HbA2 has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell para...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Feng, Pinning [verfasserIn] Li, Yuzhe [verfasserIn] Liao, Zhihao [verfasserIn] Yao, Zhenrong [verfasserIn] Lin, Wenbin [verfasserIn] Xie, Shuhua [verfasserIn] Hu, Beini [verfasserIn] Huang, Chencui [verfasserIn] Liu, Wei [verfasserIn] Xu, Hongxu [verfasserIn] Liu, Min [verfasserIn] Gan, Wenjia [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Clinica chimica acta - Amsterdam [u.a.] : Elsevier Science, 1956, 525, Seite 1-5 |
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| Übergeordnetes Werk: |
volume:525 ; pages:1-5 |
| DOI / URN: |
10.1016/j.cca.2021.12.003 |
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| Katalog-ID: |
ELV007219024 |
|---|
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| 024 | 7 | |a 10.1016/j.cca.2021.12.003 |2 doi | |
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| 100 | 1 | |a Feng, Pinning |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA |
| 264 | 1 | |c 2021 | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Background: Since screening of α-thalassemia carriers by low HbA2 has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell parameters to identify α-thalassemia carriers from low HbA2 patients.Methods: Laboratory data of 1213 patients with low HbA2 used for modeling was randomly divided into the training set (849 of 1213, 70%) and the internal validation set (364 of 1213, 30%). In addition, an external data set (n = 399) was used for model validation. Fourteen machine learning methods were applied to construct a discriminant model. Performance was evaluated with accuracy, sensitivity, specificity, etc. and compared with 7 previously published discriminant function formulae.Results: The optimal model was based on random forest with 5 clinical features. The PPV of the model was more than twice the PPV of HbA2, and the model had a high negative predictive value (NPV) at the same time. Compared with seven formulae in screening of α-thalassemia carriers, the model had a better accuracy (0.915), specificity (0.967), NPV (0.901), PPV (0.942) and area under the receiver operating characteristic curve (AUC, 0.948) in the independent test set.Conclusion: Use of a random forest-based model enables rapid discrimination of α-thalassemia carriers from low HbA2 cases. | ||
| 650 | 4 | |a Low HbA | |
| 650 | 4 | |a α-thalassemia carrier | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Red blood cell parameters | |
| 650 | 4 | |a Discriminant model | |
| 700 | 1 | |a Li, Yuzhe |e verfasserin |4 aut | |
| 700 | 1 | |a Liao, Zhihao |e verfasserin |4 aut | |
| 700 | 1 | |a Yao, Zhenrong |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Wenbin |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Shuhua |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Beini |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Chencui |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Hongxu |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Min |e verfasserin |4 aut | |
| 700 | 1 | |a Gan, Wenjia |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Clinica chimica acta |d Amsterdam [u.a.] : Elsevier Science, 1956 |g 525, Seite 1-5 |h Online-Ressource |w (DE-627)306654423 |w (DE-600)1499920-1 |w (DE-576)094531242 |x 1873-3492 |7 nnns |
| 773 | 1 | 8 | |g volume:525 |g pages:1-5 |
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| 912 | |a GBV_ILN_4393 | ||
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35.00 44.46 |
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2021 |
| allfields |
10.1016/j.cca.2021.12.003 doi (DE-627)ELV007219024 (ELSEVIER)S0009-8981(21)00425-3 DE-627 ger DE-627 rda eng 540 610 DE-600 35.00 bkl 44.46 bkl Feng, Pinning verfasserin aut An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Since screening of α-thalassemia carriers by low HbA2 has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell parameters to identify α-thalassemia carriers from low HbA2 patients.Methods: Laboratory data of 1213 patients with low HbA2 used for modeling was randomly divided into the training set (849 of 1213, 70%) and the internal validation set (364 of 1213, 30%). In addition, an external data set (n = 399) was used for model validation. Fourteen machine learning methods were applied to construct a discriminant model. Performance was evaluated with accuracy, sensitivity, specificity, etc. and compared with 7 previously published discriminant function formulae.Results: The optimal model was based on random forest with 5 clinical features. The PPV of the model was more than twice the PPV of HbA2, and the model had a high negative predictive value (NPV) at the same time. Compared with seven formulae in screening of α-thalassemia carriers, the model had a better accuracy (0.915), specificity (0.967), NPV (0.901), PPV (0.942) and area under the receiver operating characteristic curve (AUC, 0.948) in the independent test set.Conclusion: Use of a random forest-based model enables rapid discrimination of α-thalassemia carriers from low HbA2 cases. Low HbA α-thalassemia carrier Machine learning Red blood cell parameters Discriminant model Li, Yuzhe verfasserin aut Liao, Zhihao verfasserin aut Yao, Zhenrong verfasserin aut Lin, Wenbin verfasserin aut Xie, Shuhua verfasserin aut Hu, Beini verfasserin aut Huang, Chencui verfasserin aut Liu, Wei verfasserin aut Xu, Hongxu verfasserin aut Liu, Min verfasserin aut Gan, Wenjia verfasserin aut Enthalten in Clinica chimica acta Amsterdam [u.a.] : Elsevier Science, 1956 525, Seite 1-5 Online-Ressource (DE-627)306654423 (DE-600)1499920-1 (DE-576)094531242 1873-3492 nnns volume:525 pages:1-5 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.00 Chemie: Allgemeines 44.46 Klinische Pathologie AR 525 1-5 |
| spelling |
10.1016/j.cca.2021.12.003 doi (DE-627)ELV007219024 (ELSEVIER)S0009-8981(21)00425-3 DE-627 ger DE-627 rda eng 540 610 DE-600 35.00 bkl 44.46 bkl Feng, Pinning verfasserin aut An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Since screening of α-thalassemia carriers by low HbA2 has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell parameters to identify α-thalassemia carriers from low HbA2 patients.Methods: Laboratory data of 1213 patients with low HbA2 used for modeling was randomly divided into the training set (849 of 1213, 70%) and the internal validation set (364 of 1213, 30%). In addition, an external data set (n = 399) was used for model validation. Fourteen machine learning methods were applied to construct a discriminant model. Performance was evaluated with accuracy, sensitivity, specificity, etc. and compared with 7 previously published discriminant function formulae.Results: The optimal model was based on random forest with 5 clinical features. The PPV of the model was more than twice the PPV of HbA2, and the model had a high negative predictive value (NPV) at the same time. Compared with seven formulae in screening of α-thalassemia carriers, the model had a better accuracy (0.915), specificity (0.967), NPV (0.901), PPV (0.942) and area under the receiver operating characteristic curve (AUC, 0.948) in the independent test set.Conclusion: Use of a random forest-based model enables rapid discrimination of α-thalassemia carriers from low HbA2 cases. Low HbA α-thalassemia carrier Machine learning Red blood cell parameters Discriminant model Li, Yuzhe verfasserin aut Liao, Zhihao verfasserin aut Yao, Zhenrong verfasserin aut Lin, Wenbin verfasserin aut Xie, Shuhua verfasserin aut Hu, Beini verfasserin aut Huang, Chencui verfasserin aut Liu, Wei verfasserin aut Xu, Hongxu verfasserin aut Liu, Min verfasserin aut Gan, Wenjia verfasserin aut Enthalten in Clinica chimica acta Amsterdam [u.a.] : Elsevier Science, 1956 525, Seite 1-5 Online-Ressource (DE-627)306654423 (DE-600)1499920-1 (DE-576)094531242 1873-3492 nnns volume:525 pages:1-5 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.00 Chemie: Allgemeines 44.46 Klinische Pathologie AR 525 1-5 |
| allfields_unstemmed |
10.1016/j.cca.2021.12.003 doi (DE-627)ELV007219024 (ELSEVIER)S0009-8981(21)00425-3 DE-627 ger DE-627 rda eng 540 610 DE-600 35.00 bkl 44.46 bkl Feng, Pinning verfasserin aut An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Since screening of α-thalassemia carriers by low HbA2 has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell parameters to identify α-thalassemia carriers from low HbA2 patients.Methods: Laboratory data of 1213 patients with low HbA2 used for modeling was randomly divided into the training set (849 of 1213, 70%) and the internal validation set (364 of 1213, 30%). In addition, an external data set (n = 399) was used for model validation. Fourteen machine learning methods were applied to construct a discriminant model. Performance was evaluated with accuracy, sensitivity, specificity, etc. and compared with 7 previously published discriminant function formulae.Results: The optimal model was based on random forest with 5 clinical features. The PPV of the model was more than twice the PPV of HbA2, and the model had a high negative predictive value (NPV) at the same time. Compared with seven formulae in screening of α-thalassemia carriers, the model had a better accuracy (0.915), specificity (0.967), NPV (0.901), PPV (0.942) and area under the receiver operating characteristic curve (AUC, 0.948) in the independent test set.Conclusion: Use of a random forest-based model enables rapid discrimination of α-thalassemia carriers from low HbA2 cases. Low HbA α-thalassemia carrier Machine learning Red blood cell parameters Discriminant model Li, Yuzhe verfasserin aut Liao, Zhihao verfasserin aut Yao, Zhenrong verfasserin aut Lin, Wenbin verfasserin aut Xie, Shuhua verfasserin aut Hu, Beini verfasserin aut Huang, Chencui verfasserin aut Liu, Wei verfasserin aut Xu, Hongxu verfasserin aut Liu, Min verfasserin aut Gan, Wenjia verfasserin aut Enthalten in Clinica chimica acta Amsterdam [u.a.] : Elsevier Science, 1956 525, Seite 1-5 Online-Ressource (DE-627)306654423 (DE-600)1499920-1 (DE-576)094531242 1873-3492 nnns volume:525 pages:1-5 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.00 Chemie: Allgemeines 44.46 Klinische Pathologie AR 525 1-5 |
| allfieldsGer |
10.1016/j.cca.2021.12.003 doi (DE-627)ELV007219024 (ELSEVIER)S0009-8981(21)00425-3 DE-627 ger DE-627 rda eng 540 610 DE-600 35.00 bkl 44.46 bkl Feng, Pinning verfasserin aut An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Since screening of α-thalassemia carriers by low HbA2 has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell parameters to identify α-thalassemia carriers from low HbA2 patients.Methods: Laboratory data of 1213 patients with low HbA2 used for modeling was randomly divided into the training set (849 of 1213, 70%) and the internal validation set (364 of 1213, 30%). In addition, an external data set (n = 399) was used for model validation. Fourteen machine learning methods were applied to construct a discriminant model. Performance was evaluated with accuracy, sensitivity, specificity, etc. and compared with 7 previously published discriminant function formulae.Results: The optimal model was based on random forest with 5 clinical features. The PPV of the model was more than twice the PPV of HbA2, and the model had a high negative predictive value (NPV) at the same time. Compared with seven formulae in screening of α-thalassemia carriers, the model had a better accuracy (0.915), specificity (0.967), NPV (0.901), PPV (0.942) and area under the receiver operating characteristic curve (AUC, 0.948) in the independent test set.Conclusion: Use of a random forest-based model enables rapid discrimination of α-thalassemia carriers from low HbA2 cases. Low HbA α-thalassemia carrier Machine learning Red blood cell parameters Discriminant model Li, Yuzhe verfasserin aut Liao, Zhihao verfasserin aut Yao, Zhenrong verfasserin aut Lin, Wenbin verfasserin aut Xie, Shuhua verfasserin aut Hu, Beini verfasserin aut Huang, Chencui verfasserin aut Liu, Wei verfasserin aut Xu, Hongxu verfasserin aut Liu, Min verfasserin aut Gan, Wenjia verfasserin aut Enthalten in Clinica chimica acta Amsterdam [u.a.] : Elsevier Science, 1956 525, Seite 1-5 Online-Ressource (DE-627)306654423 (DE-600)1499920-1 (DE-576)094531242 1873-3492 nnns volume:525 pages:1-5 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.00 Chemie: Allgemeines 44.46 Klinische Pathologie AR 525 1-5 |
| allfieldsSound |
10.1016/j.cca.2021.12.003 doi (DE-627)ELV007219024 (ELSEVIER)S0009-8981(21)00425-3 DE-627 ger DE-627 rda eng 540 610 DE-600 35.00 bkl 44.46 bkl Feng, Pinning verfasserin aut An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Since screening of α-thalassemia carriers by low HbA2 has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell parameters to identify α-thalassemia carriers from low HbA2 patients.Methods: Laboratory data of 1213 patients with low HbA2 used for modeling was randomly divided into the training set (849 of 1213, 70%) and the internal validation set (364 of 1213, 30%). In addition, an external data set (n = 399) was used for model validation. Fourteen machine learning methods were applied to construct a discriminant model. Performance was evaluated with accuracy, sensitivity, specificity, etc. and compared with 7 previously published discriminant function formulae.Results: The optimal model was based on random forest with 5 clinical features. The PPV of the model was more than twice the PPV of HbA2, and the model had a high negative predictive value (NPV) at the same time. Compared with seven formulae in screening of α-thalassemia carriers, the model had a better accuracy (0.915), specificity (0.967), NPV (0.901), PPV (0.942) and area under the receiver operating characteristic curve (AUC, 0.948) in the independent test set.Conclusion: Use of a random forest-based model enables rapid discrimination of α-thalassemia carriers from low HbA2 cases. Low HbA α-thalassemia carrier Machine learning Red blood cell parameters Discriminant model Li, Yuzhe verfasserin aut Liao, Zhihao verfasserin aut Yao, Zhenrong verfasserin aut Lin, Wenbin verfasserin aut Xie, Shuhua verfasserin aut Hu, Beini verfasserin aut Huang, Chencui verfasserin aut Liu, Wei verfasserin aut Xu, Hongxu verfasserin aut Liu, Min verfasserin aut Gan, Wenjia verfasserin aut Enthalten in Clinica chimica acta Amsterdam [u.a.] : Elsevier Science, 1956 525, Seite 1-5 Online-Ressource (DE-627)306654423 (DE-600)1499920-1 (DE-576)094531242 1873-3492 nnns volume:525 pages:1-5 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 35.00 Chemie: Allgemeines 44.46 Klinische Pathologie AR 525 1-5 |
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Enthalten in Clinica chimica acta 525, Seite 1-5 volume:525 pages:1-5 |
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Low HbA α-thalassemia carrier Machine learning Red blood cell parameters Discriminant model |
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Feng, Pinning @@aut@@ Li, Yuzhe @@aut@@ Liao, Zhihao @@aut@@ Yao, Zhenrong @@aut@@ Lin, Wenbin @@aut@@ Xie, Shuhua @@aut@@ Hu, Beini @@aut@@ Huang, Chencui @@aut@@ Liu, Wei @@aut@@ Xu, Hongxu @@aut@@ Liu, Min @@aut@@ Gan, Wenjia @@aut@@ |
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2021-01-01T00:00:00Z |
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Feng, Pinning |
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Feng, Pinning ddc 540 bkl 35.00 bkl 44.46 misc Low HbA misc α-thalassemia carrier misc Machine learning misc Red blood cell parameters misc Discriminant model An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA |
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540 610 DE-600 35.00 bkl 44.46 bkl An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA Low HbA α-thalassemia carrier Machine learning Red blood cell parameters Discriminant model |
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ddc 540 bkl 35.00 bkl 44.46 misc Low HbA misc α-thalassemia carrier misc Machine learning misc Red blood cell parameters misc Discriminant model |
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ddc 540 bkl 35.00 bkl 44.46 misc Low HbA misc α-thalassemia carrier misc Machine learning misc Red blood cell parameters misc Discriminant model |
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An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA |
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An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA |
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Feng, Pinning |
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Feng, Pinning Li, Yuzhe Liao, Zhihao Yao, Zhenrong Lin, Wenbin Xie, Shuhua Hu, Beini Huang, Chencui Liu, Wei Xu, Hongxu Liu, Min Gan, Wenjia |
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10.1016/j.cca.2021.12.003 |
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an online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low hba |
| title_auth |
An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA |
| abstract |
Background: Since screening of α-thalassemia carriers by low HbA2 has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell parameters to identify α-thalassemia carriers from low HbA2 patients.Methods: Laboratory data of 1213 patients with low HbA2 used for modeling was randomly divided into the training set (849 of 1213, 70%) and the internal validation set (364 of 1213, 30%). In addition, an external data set (n = 399) was used for model validation. Fourteen machine learning methods were applied to construct a discriminant model. Performance was evaluated with accuracy, sensitivity, specificity, etc. and compared with 7 previously published discriminant function formulae.Results: The optimal model was based on random forest with 5 clinical features. The PPV of the model was more than twice the PPV of HbA2, and the model had a high negative predictive value (NPV) at the same time. Compared with seven formulae in screening of α-thalassemia carriers, the model had a better accuracy (0.915), specificity (0.967), NPV (0.901), PPV (0.942) and area under the receiver operating characteristic curve (AUC, 0.948) in the independent test set.Conclusion: Use of a random forest-based model enables rapid discrimination of α-thalassemia carriers from low HbA2 cases. |
| abstractGer |
Background: Since screening of α-thalassemia carriers by low HbA2 has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell parameters to identify α-thalassemia carriers from low HbA2 patients.Methods: Laboratory data of 1213 patients with low HbA2 used for modeling was randomly divided into the training set (849 of 1213, 70%) and the internal validation set (364 of 1213, 30%). In addition, an external data set (n = 399) was used for model validation. Fourteen machine learning methods were applied to construct a discriminant model. Performance was evaluated with accuracy, sensitivity, specificity, etc. and compared with 7 previously published discriminant function formulae.Results: The optimal model was based on random forest with 5 clinical features. The PPV of the model was more than twice the PPV of HbA2, and the model had a high negative predictive value (NPV) at the same time. Compared with seven formulae in screening of α-thalassemia carriers, the model had a better accuracy (0.915), specificity (0.967), NPV (0.901), PPV (0.942) and area under the receiver operating characteristic curve (AUC, 0.948) in the independent test set.Conclusion: Use of a random forest-based model enables rapid discrimination of α-thalassemia carriers from low HbA2 cases. |
| abstract_unstemmed |
Background: Since screening of α-thalassemia carriers by low HbA2 has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell parameters to identify α-thalassemia carriers from low HbA2 patients.Methods: Laboratory data of 1213 patients with low HbA2 used for modeling was randomly divided into the training set (849 of 1213, 70%) and the internal validation set (364 of 1213, 30%). In addition, an external data set (n = 399) was used for model validation. Fourteen machine learning methods were applied to construct a discriminant model. Performance was evaluated with accuracy, sensitivity, specificity, etc. and compared with 7 previously published discriminant function formulae.Results: The optimal model was based on random forest with 5 clinical features. The PPV of the model was more than twice the PPV of HbA2, and the model had a high negative predictive value (NPV) at the same time. Compared with seven formulae in screening of α-thalassemia carriers, the model had a better accuracy (0.915), specificity (0.967), NPV (0.901), PPV (0.942) and area under the receiver operating characteristic curve (AUC, 0.948) in the independent test set.Conclusion: Use of a random forest-based model enables rapid discrimination of α-thalassemia carriers from low HbA2 cases. |
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An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA |
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Li, Yuzhe Liao, Zhihao Yao, Zhenrong Lin, Wenbin Xie, Shuhua Hu, Beini Huang, Chencui Liu, Wei Xu, Hongxu Liu, Min Gan, Wenjia |
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7.400199 |