• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>The Internet Journal of Tropical Medicine >Application of Correlation & Regression Tree (CART) for management of Malaria in Arunachal Pradesh, India
【24h】

Application of Correlation & Regression Tree (CART) for management of Malaria in Arunachal Pradesh, India

机译：相关和回归树（CART）在印度阿鲁纳恰尔邦疟疾管理中的应用

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

摘要

Malaria is a focal disease with multitudinous variations in its epidemiological pattern in relation to topographical features. The present paper demonstrates the application of CART (Classification & Regression Trees) for control of malaria in Arunachal Pradesh, India. Baseline epidemiological data of 12 districts of Arunachal Pradesh was employed for deriving prediction rules. The data was categorized into 2 different aspects, namely (1) Epidemiological (2) Meteorological. The intricate and complex interactions that exist between diverse input data sets, as they relate to the target features, are learned and modeled through exhaustive analysis. Predictor variables (maximum temperature, minimum temperature, rainfall, relative humidity, number of rainy days and month) were ranked by CART according to their influence on the target variable (MPI). Application of these easily conceptualized rules, rather than more abstract epidemiological principles, enables even non-specialists to gain an understanding of the malaria problem and in forecasting the malaria transmission dynamics to formulate the intervention strategies to combat malaria effectively. Introduction Malaria, the third leading cause of death attributable to an infectious disease worldwide, has plagued mankind for countless generations. The problem of Malaria is deeply entrenched in more than 90 countries of the world (WHO, 1998) and result in approximately 300 million acute illnesses and at least one million deaths annually (WHO, 1999). India being a tropical country is a malarial paradise with annual burden estimated to be nearly 2 to 2.5 million cases. North-Eastern region of India is in the Indo-Chinese hill zone of Macdonald's classification of stable malaria (MacDonald, 1957) and contributes nearly 9% of total malaria cases in India (Shiv Lal et al, 2000). In this region, perennial transmission of malaria slashes potential economic growth and thus is a major impediment to the overall development and progress of these areas. Despite several anti-malaria programmes, this region has seen little tangible progress in alleviating the burden of malaria (Mohapatra et al, 1998; Sen et al, 1994). Apparently, there are definite inadequacies that continue to dampen the spirit of public health specialists even since the halcyon days of malaria eradication. On closer scrutiny, operational difficulties stemming from the financial constraints and lack of definite knowledge about the malaria transmission trends are hampering the effective malaria control in the North-Eastern region (Mohapatra et al, 2003). Inaccessible areas owing to floods bear the maximum brunt of malaria. Main factors leading to failures in combating malaria in such regions are predominance of Plasmodium falciparum (Sharma et al, 1999, Dev et al, 2003), difficult terrain (Yadava & Sharma, 1995), favorable eco-climatic conditions (Sharma et al, 1996), lack of proper execution of control operations and wide communication gap between health researchers and policy makers. The problem of drug resistance (Kondrashin et al, 1987; Satyanarayana et al, 1991; Mohapatra et al, 2003), exophilic and exophagic vector behavior and high efficiency of vectors (Sharma et al, 1996, Dev et al, 2003) further aggravate the gravity of complex situation. Due to these various factors encountered in the North-Eastern region, malaria continues to present health services with an immensely difficult and complex challenge. Despite committed attempts for widespread implementation of conventional control methods of recognized efficacy, persistent malaria transmission in this region has underlined the need for alternate strategies to tackle the problem. The highly focal nature of malaria requires targeting of interventions to specific regions at appropriate time. Data mining applications have been successfully used in the past for the spatial clustering of endemic zones (Murty and Neelima Arora, 2007 a, b) prediction of disease outbreak (Kumar
机译：疟疾是一种局灶性疾病，与地形特征有关，其流行病学模式存在多种变化。本文演示了CART（分类树和回归树）在印度阿鲁纳恰尔邦控制疟疾中的应用。利用阿鲁纳恰尔邦12个地区的基线流行病学数据推导了预测规则。数据分为两个不同的方面，即（1）流行病学（2）气象学。通过详尽的分析来学习和建模各种输入数据集之间与目标特征相关的复杂而复杂的交互。根据预测变量对目标变量（MPI）的影响，通过CART对预测变量（最高温度，最低温度，降雨量，相对湿度，雨天和月份的数量）进行排名。这些容易概念化的规则的应用，而不是更抽象的流行病学原理，甚至使非专业人员也可以了解疟疾问题，并预测疟疾传播动态，从而制定有效地抗击疟疾的干预策略。引言疟疾是世界范围内第三种导致传染病的主要死亡原因，无数代人困扰着人类。疟疾问题在世界上90多个国家中根深蒂固（世界卫生组织，1998年），每年导致约3亿例急性疾病和至少100万人死亡（世卫组织，1999年）。印度是一个热带国家，是一个疟疾天堂，每年的负担估计约为2至250万例。印度的东北地区位于麦克唐纳分类为稳定疟疾的印度支那山地带（MacDonald，1957），占印度总疟疾病例的近9％（Shiv Lal等，2000）。在该区域，疟疾的常年传播极大地阻碍了潜在的经济增长，因此成为这些地区总体发展和进步的主要障碍。尽管有数项抗疟方案，但该地区在减轻疟疾负担方面没有取得明显进展（Mohapatra等，1998; Sen等，1994）。显然，自从消灭疟疾的太平日子以来，仍然存在着一定的不足之处，这些不足仍继续削弱公共卫生专家的精神。经过更严格的审查，由于资金拮据和对疟疾传播趋势缺乏确切知识而造成的运营困难正在阻碍东北地区的有效疟疾控制（Mohapatra等，2003）。由于洪水而无法进入的地区首当其冲。在这些地区导致抗击疟疾失败的主要因素是恶性疟原虫占主导地位（Sharma等，1999; Dev等，2003），艰难的地形（Yadava＆Sharma，1995），良好的生态气候条件（Sharma等，1999）。 （1996年），缺乏适当的控制措施执行，卫生研究人员与决策者之间的沟通差距很大。耐药性问题（Kondrashin等人，1987; Satyanarayana等人，1991; Mohapatra等人，2003），外生性和外生性媒介行为以及媒介的高效性（Sharma等人，1996，Dev等人，2003）进一步加剧了这一问题。复杂情况的严重性。由于东北地区遇到各种因素，疟疾继续为卫生服务提供了巨大的困难和复杂的挑战。尽管已作出广泛尝试，以广泛实施公认的有效常规控制方法，但该地区持续的疟疾传播仍凸显了解决这一问题的替代战略的必要性。疟疾的高度集中性要求在适当的时候将干预措施针对特定地区。过去，数据挖掘应用已成功用于流行区的空间聚类（Murty和Neelima Arora，2007 a，b）疾病暴发的预测（库马尔）

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