PSO/ACO algorithm-based risk assessment of human neural tube defects in Heshun County, China / 生物医学与环境科学(英文)
Biomedical and Environmental Sciences
; (12): 569-576, 2012.
Article
Dans En
| WPRIM
| ID: wpr-320397
Responsable en Bibliothèque :
WPRO
ABSTRACT
<p><b>OBJECTIVE</b>To develop a new technique for assessing the risk of birth defects, which are a major cause of infant mortality and disability in many parts of the world.</p><p><b>METHODS</b>The region of interest in this study was Heshun County, the county in China with the highest rate of neural tube defects (NTDs). A hybrid particle swarm optimization/ant colony optimization (PSO/ACO) algorithm was used to quantify the probability of NTDs occurring at villages with no births. The hybrid PSO/ACO algorithm is a form of artificial intelligence adapted for hierarchical classification. It is a powerful technique for modeling complex problems involving impacts of causes.</p><p><b>RESULTS</b>The algorithm was easy to apply, with the accuracy of the results being 69.5%±7.02% at the 95% confidence level.</p><p><b>CONCLUSION</b>The proposed method is simple to apply, has acceptable fault tolerance, and greatly enhances the accuracy of calculations.</p>
Texte intégral:
1
Indice:
WPRIM
Sujet Principal:
Algorithmes
/
Intelligence artificielle
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Chine
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Épidémiologie
/
Facteurs de risque
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Exposition environnementale
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Modèles biologiques
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Anomalies du tube neural
Type d'étude:
Etiology_studies
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Prognostic_studies
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Risk_factors_studies
Limites du sujet:
Humans
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Newborn
Pays comme sujet:
Asia
langue:
En
Texte intégral:
Biomedical and Environmental Sciences
Année:
2012
Type:
Article