Detection of Chronic Kidney Disease using Machine Learning Algorithms with Least Number of Predictors
Detection of Chronic Kidney Disease using Machine Learning Algorithms with Least Number of Predictors
复制标题
使用具有最少预测变量的机器学习算法检测慢性肾脏病
DOI：
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发表时间：
2019
影响因子：
0.9
通讯作者：
T. Ward
中科院分区：
文献类型：
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作者：
M. Almasoud;T. Ward
慢性肾脏病（CKD）是最严重的健康问题之一，因为它的患病率不断增加。在本文中，我们的目标是测试机器学习算法使用最小特征子集预测慢性肾脏疾病的能力。已经做了几个统计测试，以消除冗余的功能，如方差分析测试，皮尔森的相关性，和Cramer的V检验。逻辑回归，支持向量机，随机森林和梯度增强算法已经使用10倍交叉验证进行了训练和测试。根据梯度提升分类器的F1-measure，我们实现了99.1的准确率。此外，我们发现血红蛋白在检测CKD时对于随机森林和梯度增强都具有更高的重要性。最后，与以前的研究相比，我们的结果是最高的，但到目前为止达到的特征数量较少。因此，我们可以通过执行三个简单的测试来检测CKD，只需26.65美元。
Chronic kidney disease (CKD) is one of the most critical health problems due to its increasing prevalence. In this paper, we aim to test the ability of machine learning algorithms for the prediction of chronic kidney disease using the smallest subset of features. Several statistical tests have been done to remove redundant features such as the ANOVA test, the Pearson’s correlation, and the Cramer’s V test. Logistic regression, support vector machines, random forest, and gradient boosting algorithms have been trained and tested using 10-fold cross-validation. We achieve an accuracy of 99.1 according to F1-measure from Gradient Boosting classifier. Also, we found that hemoglobin has higher importance for both random forest and Gradient boosting in detecting CKD. Finally, our results are among the highest compared to previous studies but with less number of features reached so far. Hence, we can detect CKD at only $26.65 by performing three simple tests.
DOI：
10.1007/3-540-45014-9
发表时间：
2000-06
期刊：
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影响因子：
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作者：
Thomas G. Dietterich
通讯作者：
Thomas G. Dietterich