LIAO Yi Lan; WANG Jin Feng; WU Ji Lei; WANG Jiao Jiao; ZHENG XiaoYing

doi:10.3967/0895-3988.2012.05.011

doi: 10.3967/0895-3988.2012.05.011

基金项目: National Natural Science Foundation of China(41101431)%the fourth installment special funding of China Postdoctoral Science Foundation(201104003)%China Postdoctoral Science Foundation(20100470004)%the State Key Funds of Social Science Project(09&ZD072)

PSO/ACO Algorithm-based Risk Assessment of Human Neural Tube Defects in Heshun County, China

The State Key Laboratory of Resources and Environmental Information System,Institute of Geographical Sciences and Nature Resources Research,Chinese academy of Sciences,Beijing 100101,China;Institute of Population Research,Peking University,Beijing 100871,China
The State Key Laboratory of Resources and Environmental Information System,Institute of Geographical Sciences and Nature Resources Research,Chinese academy of Sciences,Beijing 100101,China
Institute of Population Research,Peking University,Beijing 100871,China
The State Key Laboratory of Resources and Environmental Information System,Institute of Geographical Sciences and Nature Resources Research,Chinese academy of Sciences,Beijing 100101,China;College of Geoscience and Surveying Engineering,China University of Mining & Technology,Beijing 100083,China

Funds: National Natural Science Foundation of China(41101431)%the fourth installment special funding of China Postdoctoral Science Foundation(201104003)%China Postdoctoral Science Foundation(20100470004)%the State Key Funds of Social Science Project(09&ZD072)

摘要: Objective 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.Methods 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.Results The algorithm was easy to apply, with the accuracy of the results being 69.5%±7.02% at the 95% confidence level.Conclusion The proposed method is simple to apply, has acceptable fault tolerance, and greatly enhances the accuracy of calculations.
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Abstract: Objective 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.Methods 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.Results The algorithm was easy to apply, with the accuracy of the results being 69.5%±7.02% at the 95% confidence level.Conclusion The proposed method is simple to apply, has acceptable fault tolerance, and greatly enhances the accuracy of calculations.
- Neural tube birth defects /
- GIS /
- PSO/ACO algorithm /
- Hierarchical classification /
- Risk map

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doi: 10.3967/0895-3988.2012.05.011

PSO/ACO Algorithm-based Risk Assessment of Human Neural Tube Defects in Heshun County, China

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PSO/ACO Algorithm-based Risk Assessment of Human Neural Tube Defects in Heshun County, China

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PSO/ACO Algorithm-based Risk Assessment of Human Neural Tube Defects in Heshun County, China

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doi: 10.3967/0895-3988.2012.05.011

PSO/ACO Algorithm-based Risk Assessment of Human Neural Tube Defects in Heshun County, China

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PSO/ACO Algorithm-based Risk Assessment of Human Neural Tube Defects in Heshun County, China

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