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首页> 外文会议>2017 International Conference on Inventive Computing and Informatics >Prediction performance of individual and ensemble learners for chronic kidney disease
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Prediction performance of individual and ensemble learners for chronic kidney disease

机译：个人和整体学习者对慢性肾脏疾病的预测表现

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Automating the process of predicting diseases prove assistive and time-saving for a practitioner in the field of medical diagnosis. The accurate prediction of any disease not only helps the patients know about their health but also helps the doctors in medication suggestion well in advance. In today's lifestyle, advance knowledge about health and proper care can add a number of living days to a patient's life. In this paper, the prediction of chronic kidney disease (CKD) is performed using individual and ensemble learners. The experiments are performed on CKD dataset was taken from UCI repository. The three different classifiers from individual classifiers, namely, Naive Bayes(NB), minimal sequential optimization (SMO), J48, and three ensemble classifiers, namely, Random Forest (RF), bagging, AdaBoost respectively are used for prediction. We have used the open source, weka tool, for all the experiments. The results are evaluated using accuracy, precision, recall, F-measure and ROC performance measures. The results suggested that the decision tree based individual learner (J48) and random forest from ensemble classifier respectively perform better than the other classifiers.
机译：在医学诊断领域，自动化疾病预测过程证明了从业者的辅助和节省时间。对任何疾病的准确预测不仅可以帮助患者了解其健康状况，还可以帮助医生提前进行用药建议。在当今的生活方式中，对健康和适当护理的深入了解可以增加患者的生活时间。在本文中，对慢性肾脏疾病（CKD）的预测是通过个体和整体学习者来进行的。对从UCI资料库获取的CKD数据集进行了实验。来自单个分类器的三个不同分类器，即朴素贝叶斯（NB），最小顺序优化（SMO），J48和三个整体分类器，分别是随机森林（RF），装袋，AdaBoost，用于预测。我们在所有实验中都使用了开源的weka工具。使用准确性，精度，召回率，F量度和ROC性能量度对结果进行评估。结果表明，基于决策树的个体学习器（J48）和来自集成分类器的随机森林分别比其他分类器表现更好。

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