期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：8
页码：89-96
出版社：Science and Information Society (SAI)
摘要：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.
