• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Information Systems and Technologies >Classification model analysis for the prediction of leptospirosis cases
【24h】

Classification model analysis for the prediction of leptospirosis cases

机译：预测钩端螺旋体病病例的分类模型分析

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Leptospirosis is a disease that affects mainly low-income populations, with an incidence of 500,000 cases per year worldwide[1]. The disease has symptoms often confused with other febrile syndromes, such as dengue, influenza and viral hepatitis. Improved diagnosis of patients with leptospirosis is very important for health professionals, epidemiological surveillance and primarily for rapid evaluation and appropriate treatment of patients. In this work, an analysis of the data mining techniques classification was performed, evaluating algorithms of the methods of Decision Tree, Classification Rules and Bayesian Classification. Of these, JRip was the model with the best performance, yielding 85% sensitivity and 81% specificity. The algorithms successfully predicted the disease and may represent a new tool to assist health professionals in the daily hospital routine, especially in endemic areas for leptospirosis, accelerating targeted treatment, and minimizing disease exacerbation and mortality.
机译：钩端螺旋体病是一种主要影响低收入人群的疾病，全世界每年发病50万例[1]。该病的症状通常与其他高热综合征相混淆，例如登革热，流感和病毒性肝炎。改善钩端螺旋体病患者的诊断对卫生专业人员，流行病学监测以及主要是对患者进行快速评估和适当治疗非常重要。在这项工作中，对数据挖掘技术的分类进行了分析，评估了决策树，分类规则和贝叶斯分类方法的算法。其中，JRip是性能最佳的模型，灵敏度为85％，特异性为81％。该算法成功地预测了疾病，并且可能代表了一种新的工具，可以帮助卫生专业人员在医院的日常工作中，特别是在钩端螺旋体病的流行地区，加速靶向治疗并最大程度地降低疾病加重和死亡率。

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