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）

<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>

Comparison on the performance of both temporal and temporal-spatial models for outbreak detection through China Infectious Diseases Automated-alert and Response System(CIDARS) / 中华流行病学杂志

Objective To analyze the pilot results of both temporal and temporal-spatial models in outbreaks detection in China Infectious Diseases Automated-alert and Response System (CIDARS)to further improve the system. Methods The amount of signal, sensitivity, false alarm rate and time to detection regarding these two models of CIDARS, were analyzed from December 6,2009 to December 5,2010 in 221 pilot counties of 20 provinces. Results The sensitivity of these two models was equal(both 98.15%). However, when comparing to the temporal model, the temporal-spatial model had a 59.86% reduction on the signals(15 702)while the false alarm rate of the temporal-spatial model(0.73%)was lower than the temporal model(1.79%), and the time to detection of the temporal-spatial model(0 day)was also 1 day shorter than the temporal model.Conclusion Comparing to the temporal model, the temporal-spatial model of CIDARS seemed to be better performed on outbreak detection.

Prediction of neural tube defect using support vector machine / 生物医学与环境科学（英文）

<p><b>OBJECTIVE</b>To predict neural tube birth defect (NTD) using support vector machine (SVM).</p><p><b>METHOD</b>The dataset in the pilot area was divided into non overlaid training set and testing set. SVM was trained using the training set and the trained SVM was then used to predict the classification of NTD.</p><p><b>RESULT</b>NTD rate was predicted at village level in the pilot area. The accuracy of the prediction was 71.50% for the training dataset and 68.57% for the test dataset respectively.</p><p><b>CONCLUSION</b>Results from this study have shown that SVM is applicable to the prediction of NTD.</p>