Performance analysis of machine learning algorithms and screening formulae for ß-thalassemia trait screening of Indian antenatal women.
Int J Med Inform
; 167: 104866, 2022 11.
Article
em En
| MEDLINE
| ID: mdl-36174416
ABSTRACT
BACKGROUND:
Currently, more than forty discrimination formulae based on red blood cell (RBC) parameters and some supervised machine learning algorithms (MLAs) have been recommended for ß-thalassemia trait (BTT) screening. The present study was aimed to evaluate and compare the performance of 26 such formulae and 13 MLAs on antenatal woman data with a recently developed formula SCSBTT, which is available for evaluation in over seventy countries as an Android app, called SUSOKA[16].METHODS:
A diagnostic database of 2942 antenatal females were collected from PGIMER, Chandigarh, India and was used for this analysis. The data set consists of hypochromic microcytic anemia, BTT, Hemoglobin E trait, double heterozygote for Hemoglobin S and BTT, heterozygote for Hemoglobin D Punjab and normal subjects. Performance of the formulae and the MLAs were assessed by Sensitivity, Specificity, Youden's Index, and AUC-ROC measures. A final recommendation was made from the ranking obtained through two Multiple Criteria Decision-Making (MCDM) techniques, namely, Simultaneous Evaluation of Criteria and Alternatives (SECA) and TOPSIS.RESULTS:
It was observed that Extreme Learning Machine (ELM) and Gradient Boosting Classifier (GBC) showed maximum Youden's index and AUC-ROC measures compared to all discriminating formulae. Sensitivity remains maximum for SCSBTT. K-means clustering and the ranking from MCDM methods show that SCSBTT, Shine & Lal and Ravanbakhsh-F4 formula ensures higher performance among all formulae. The discriminant power of some MLAs and formulae was found considerably lower than that reported in original studies.CONCLUSION:
Comparative information on MLAs can aid researchers in developing new discriminating formulae that simultaneously ensure higher sensitivity and specificity. More multi-centric verification of the formulae on heterogeneous data is indispensable. SCSBTT and Shine & Lal formula, and ELM and GBC are recommended for screening BTT based on MCDM. SCSBTT can be used with certainty as a tangible cost-saving screening tool for mass screening for antenatal women in India and other countries.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Hemoglobina E
/
Talassemia beta
/
Anemia Ferropriva
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
/
Guideline
/
Prognostic_studies
/
Screening_studies
Limite:
Female
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Humans
/
Pregnancy
Idioma:
En
Revista:
Int J Med Inform
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Índia