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PSO/ACO algorithm-based risk assessment of human neural tube defects in Heshun County, China
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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