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.
Artículo
en Inglés
| WPRIM
| ID: wpr-320397
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>
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Algoritmos
/
Inteligencia Artificial
/
China
/
Epidemiología
/
Factores de Riesgo
/
Exposición a Riesgos Ambientales
/
Modelos Biológicos
/
Defectos del Tubo Neural
Tipo de estudio:
Estudio de etiología
/
Estudio pronóstico
/
Factores de riesgo
Límite:
Humanos
/
Recién Nacido
País/Región como asunto:
Asia
Idioma:
Inglés
Revista:
Biomedical and Environmental Sciences
Año:
2012
Tipo del documento:
Artículo
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