Fuzzy association rule mining and classification for the prediction of malaria in South Korea.
Fuzzy association rule mining and classification for the prediction of malaria in South Korea.
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DOI：
10.1186/s12911-015-0170-6
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发表时间：
2015-06-18
影响因子：
3.5
通讯作者：
Lewis SH
中科院分区：
文献类型：
--
作者：
Buczak AL;Baugher B;Guven E;Ramac-Thomas LC;Elbert Y;Babin SM;Lewis SH
疟疾是世界上最流行的病媒传播疾病。准确预测疟疾爆发可能导致公共卫生干预措施，降低疾病发病率和死亡率。我们描述了一个应用程序的方法，利用模糊关联规则挖掘来创建预测模型，从韩国的流行病学，气象，气候和社会经济数据之间的关系。这些关系以规则的形式存在，从中自动选择最佳规则集并形成分类器。已经建立了两个分类器，并将其结果融合成为疟疾预测模型。未来的疟疾病例预测为低、中或高，其中这些类别分别定义为韩国某地区在两周内的病例总数为0-2、3-16和17例以上。根据用户建议，HIGH被认为是爆发。模型准确性通过阳性预测值（PPV）、灵敏度和每个类别的F分数来描述，这些分数是根据先前未用于开发模型的测试数据计算的。对于提前7-8周进行的预测，模型PPV和灵敏度分别为0.842和0.681，对于HIGH类。高级别的F0.5和F3评分（结合了联合收割机PPV和灵敏度）分别为0.804和0.694。总体FARM结果（由F分数测量）明显优于决策树，随机森林，支持向量机和霍尔特-温特斯方法获得的HIGH类。对于MEDIUM类，随机森林和FARM获得了相当的结果，FARM在F0.5时更好，而随机森林获得了更高的F3。先前描述的用于创建疾病预测模型的方法已经被修改和扩展以构建用于预测疟疾的模型。此外，还使用了一些新的投入变量，包括干预措施指标。韩国疟疾预测模型预测未来7-8周的低、中或高病例。本文证明了我们的数据驱动方法可以用于不同疾病的预测。
Malaria is the world’s most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality. We describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea. These relationships are in the form of rules, from which the best set of rules is automatically chosen and forms a classifier. Two classifiers have been built and their results fused to become a malaria prediction model. Future malaria cases are predicted as LOW, MEDIUM or HIGH, where these classes are defined as a total of 0–2, 3–16, and above 17 cases, respectively, for a region in South Korea during a two-week period. Based on user recommendations, HIGH is considered an outbreak. Model accuracy is described by Positive Predictive Value (PPV), Sensitivity, and F-score for each class, computed on test data not previously used to develop the model. For predictions made 7–8 weeks in advance, model PPV and Sensitivity are 0.842 and 0.681, respectively, for the HIGH classes. The F0.5 and F3 scores (which combine PPV and Sensitivity) are 0.804 and 0.694, respectively, for the HIGH classes. The overall FARM results (as measured by F-scores) are significantly better than those obtained by Decision Tree, Random Forest, Support Vector Machine, and Holt-Winters methods for the HIGH class. For the MEDIUM class, Random Forest and FARM obtain comparable results, with FARM being better at F0.5, and Random Forest obtaining a higher F3. A previously described method for creating disease prediction models has been modified and extended to build models for predicting malaria. In addition, some new input variables were used, including indicators of intervention measures. The South Korea malaria prediction models predict LOW, MEDIUM or HIGH cases 7–8 weeks in the future. This paper demonstrates that our data driven approach can be used for the prediction of different diseases.
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影响因子：
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作者：
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DOI：
10.4269/ajtmh.1994.50.550
发表时间：
1994-05-01
影响因子：
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