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
in English
| 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>
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Algorithms
/
Artificial Intelligence
/
China
/
Epidemiology
/
Risk Factors
/
Environmental Exposure
/
Models, Biological
/
Neural Tube Defects
Type of study:
Etiology study
/
Prognostic study
/
Risk factors
Limits:
Humans
/
Infant, Newborn
Country/Region as subject:
Asia
Language:
English
Journal:
Biomedical and Environmental Sciences
Year:
2012
Type:
Article
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