An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA2 cases.
Clin Chim Acta
; 525: 1-5, 2022 Jan 15.
Article
em En
| MEDLINE
| ID: mdl-34883090
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.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Talassemia beta
/
Talassemia alfa
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
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Prognostic_studies
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Screening_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article