A genetic algorithm for irregularly shaped spatial scan statistics
A genetic algorithm for irregularly shaped spatial scan statistics
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DOI：
10.1016/j.csda.2007.01.016
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发表时间：
2007-09-15
影响因子：
1.8
通讯作者：
Bessegato, Lupercio E.
中科院分区：
文献类型：
--
作者：
Duczmal, Luiz;Cancado, Andre L. F.;Bessegato, Lupercio E.
提出了一种基于遗传算法的不规则形状空间簇检测与推理的新方法。给出一张地图，将其划分为具有相应危险人群和病例的区域，通过快速的子代生成和对Kuldorff空间扫描统计量的有效评估来最小化与图相关的操作。采用基于几何非紧致概念的惩罚函数来避免簇几何形状的过度不规则性。该算法比模拟退火法快一个数量级，方差较小，比椭圆扫描法更灵活。对于轻微不规则的星团，它具有与模拟退火法扫描大致相同的检测能力，而对于非常不规则的星团，它的检测能力更好。讨论了该技术在巴西乳腺癌集群中的应用。(C)2007 Elsevier B.V.保留所有权利。
A new approach is presented for the detection and inference of irregularly shaped spatial clusters, using a genetic algorithm. Given a map divided into regions with corresponding populations at risk and cases, the graph-related operations are minimized by means of a fast offspring generation and efficient evaluation of Kuldorff's spatial scan statistic. A penalty function based on the geometric non-compactness concept is employed to avoid excessive irregularity of cluster geometric shape. The algorithm is an order of magnitude faster and exhibits less variance compared to the simulated annealing scan, and is more flexible than the elliptic scan. It has about the same power of detection as the simulated annealing scan for mildly irregular clusters and is superior for the very irregular ones. An application to breast cancer clusters in Brazil is discussed. (c) 2007 Elsevier B.V. All rights reserved.