title: PSO/ACO algorithm-based risk assessment of human neural tube defects in Heshun County, China.
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.
  
  
  CONCLUSIONS
  The proposed method is simple to apply, has acceptable fault tolerance, and greatly enhances the accuracy of calculations.
authors.forename: Yi Lan
authors.initials: YL
authors.lastname: Liao
authors.forename: Jin Feng
authors.initials: JF
authors.lastname: Wang
authors.forename: Ji Lei
authors.initials: JL
authors.lastname: Wu
authors.forename: Jiao Jiao
authors.initials: JJ
authors.lastname: Wang
authors.forename: Xiao Ying
authors.initials: XY
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002681:China / Q000453:epidemiology; D004781:Environmental Exposure / Q000009:adverse effects; D006801:Humans; D007231:Infant, Newborn; D008954:Models, Biological; D009436:Neural Tube Defects / Q000453:epidemiology*; D012307:Risk Factors