...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Environmental Research and Public Health >Respiratory Diseases, Malaria and Leishmaniasis: Temporal and Spatial Association with Fire Occurrences from Knowledge Discovery and Data Mining
【24h】

Respiratory Diseases, Malaria and Leishmaniasis: Temporal and Spatial Association with Fire Occurrences from Knowledge Discovery and Data Mining

机译：呼吸系统疾病，疟疾和利什曼病：来自知识发现和数据挖掘的火灾发生的时间和空间联合

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The relationship between the fires occurrences and diseases is an essential issue for making public health policy and environment protecting strategy. Thanks to the Internet, today, we have a huge amount of health data and fire occurrence reports at our disposal. The challenge, therefore, is how to deal with 4 Vs (volume, variety, velocity and veracity) associated with these data. To overcome this problem, in this paper, we propose a method that combines techniques based on Data Mining and Knowledge Discovery from Databases (KDD) to discover spatial and temporal association between diseases and the fire occurrences. Here, the case study was addressed to Malaria, Leishmaniasis and respiratory diseases in Brazil. Instead of losing a lot of time verifying the consistency of the database, the proposed method uses Decision Tree, a machine learning-based supervised classification, to perform a fast management and extract only relevant and strategic information, with the knowledge of how reliable the database is. Namely, States, Biomes and period of the year (months) with the highest rate of fires could be identified with great success rates and in few seconds. Then, the K-means, an unsupervised learning algorithms that solves the well-known clustering problem, is employed to identify the groups of cities where the fire occurrences is more expressive. Finally, the steps associated with KDD is perfomed to extract useful information from mined data. In that case, Spearman’s rank correlation coefficient, a nonparametric measure of rank correlation, is computed to infer the statistical dependence between fire occurrences and those diseases. Moreover, maps are also generated to represent the distribution of the mined data. From the results, it was possible to identify that each region showed a susceptible behaviour to some disease as well as some degree of correlation with fire outbreak, mainly in the drought period.
机译：火灾发生与疾病之间的关系是制作公共卫生政策和环境保护策略的重要问题。由于互联网，今天，我们可以随意使用大量的健康数据和火灾发生报告。因此，挑战是如何处理与这些数据相关的4个VS（卷，品种，速度和准确性）。为了克服这个问题，在本文中，我们提出了一种方法，该方法将基于数据挖掘和知识发现的技术与数据库（KDD）组合，以发现疾病与火灾发生之间的空间和时间关联。在这里，案例研究对巴西的疟疾，利什曼病和呼吸系统疾病进行了解决。而不是验证数据库的一致性，而不是失去大量时间，所提出的方法使用决策树，基于机器学习的监督分类，执行快速管理和仅提取相关和战略信息，并知识数据库多么可靠是。即，可以以巨大的成功率和几秒钟内识别出最高射击率最高的年份（月份）。然后，k-means是一种解决众所周知的聚类问题的无监督的学习算法，用于识别火灾发生更为富有表现力的城市组。最后，与KDD相关的步骤是从挖掘数据中提取有用信息的。在这种情况下，Spearman的等级相关系数是计算秩相关的非参数措施，以推断出火灾发生和这些疾病之间的统计依赖性。此外，还生成映射以表示挖掘数据的分布。从结果中，有可能识别每个区域对某些疾病的敏感行为以及火灾爆发的一定程度的相关性，主要是在干旱期间。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号