利用WEKA数据采掘工具进行登革热预测
Dengue disease prediction using weka data mining tool
摘要：
登革热是一种威胁生命的疾病，流行于几个发达国家以及发展中国家，如印度。在本文中，我们讨论了各种算法的数据采掘已用于登革热疾病的预测。数据采掘是卫生组织在生物信息学研究中用于疾病分类（例如登革热、糖尿病和癌症）的公知技术。在所提出的方法中，我们使用WEKA与10交叉验证来评估数据和比较结果。Weka拥有大量不同的机器学习和数据采掘算法。本文首先对登革热数据集进行了分类，然后通过Explorer、知识流和Experimenter接口对WEKA中不同的数据采掘技术进行了比较。此外，为了验证我们的方法，我们使用了具有108个实例的登革热数据集，但weka使用99行和18个属性来确定疾病的预测及其准确性，使用不同算法的分类来找出最佳性能。本文的主要目标是对数据进行分类，帮助用户从数据中提取有用的信息，并轻松地从中识别出合适的算法来进行准确的预测模型。从本文的研究结果可以得出结论，朴素贝叶斯和J48是分类准确率最高的算法，因为它们在99个正确分类的实例中达到了最高的准确率= 100%，最大ROC = 1，平均绝对误差最小，通过Explorer和Knowledge flow结果构建此模型所需时间最短
英文摘要：
Dengue is a life threatening disease prevalent in several developed as well as developing countries like India.In this paper we discuss various algorithm approaches of data mining that have been utilized for dengue disease prediction. Data mining is a well known technique used by health organizations for classification of diseases such as dengue, diabetes and cancer in bioinformatics research. In the proposed approach we have used WEKA with 10 cross validation to evaluate data and compare results. Weka has an extensive collection of different machine learning and data mining algorithms. In this paper we have firstly classified the dengue data set and then compared the different data mining techniques in weka through Explorer, knowledge flow and Experimenter interfaces. Furthermore in order to validate our approach we have used a dengue dataset with 108 instances but weka used 99 rows and 18 attributes to determine the prediction of disease and their accuracy using classifications of different algorithms to find out the best performance. The main objective of this paper is to classify data and assist the users in extracting useful information from data and easily identify a suitable algorithm for accurate predictive model from it. From the findings of this paper it can be concluded that Naïve Bayes and J48 are the best performance algorithms for classified accuracy because they achieved maximum accuracy= 100% with 99 correctly classified instances, maximum ROC = 1, had least mean absolute error and it took minimum time for building this model through Explorer and Knowledge flow results
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。
