交替决策树在登革热早期诊断中的应用
Alternating Decision trees for early diagnosis of dengue fever
摘要：
登革热是一种类似流感的疾病，由受感染的蚊子叮咬传播，很快成为一个主要的健康问题。利用临床和实验室特征进行及时和具有成本效益的诊断，除了为临床管理和疾病监测提供更好的依据外，还可以降低死亡率。我们希望开发一个强大的和有效的决策树为基础的方法来预测登革热疾病。我们的分析是基于患病个体的临床特征和实验室测量。我们已经开发和训练了一个具有boosting的交替决策树，并将其性能与登革热疾病诊断的C4.5算法进行了比较。在65份病历中，诊断确定53人已被确认患有登革热。基于交替决策树的算法能够使用临床和实验室数据区分登革热，正确分类的实例数为89%，F-测量值为0.86，受试者操作特征（ROC）为0.826，而C4.5的正确分类实例数为78%，F-测量值为0.738，ROC为0.617。交替的基于决策树的方法与提升已经能够预测登革热，具有比基于C4.5的决策树更高的准确度，使用简单的临床和实验室特征。需要对更大的数据集进行进一步分析，以提高交替决策树的灵敏度和特异性。
英文摘要：
Dengue fever is a flu-like illness spread by the bite of an infected mosquito which is fast emerging as a major health problem. Timely and cost effective diagnosis using clinical and laboratory features would reduce the mortality rates besides providing better grounds for clinical management and disease surveillance. We wish to develop a robust and effective decision tree based approach for predicting dengue disease. Our analysis is based on the clinical characteristics and laboratory measurements of the diseased individuals. We have developed and trained an alternating decision tree with boosting and compared its performance with C4.5 algorithm for dengue disease diagnosis. Of the 65 patient records a diagnosis establishes that 53 individuals have been confirmed to have dengue fever. An alternating decision tree based algorithm was able to differentiate the dengue fever using the clinical and laboratory data with number of correctly classified instances as 89%, F-measure of 0.86 and receiver operator characteristics (ROC) of 0.826 as compared to C4.5 having correctly classified instances as 78%,h F-measure of 0.738 and ROC of 0.617 respectively. Alternating decision tree based approach with boosting has been able to predict dengue fever with a higher degree of accuracy than C4.5 based decision tree using simple clinical and laboratory features. Further analysis on larger data sets is required to improve the sensitivity and specificity of the alternating decision trees.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
