Abstract:
Chronic kidney disease (CKD) is a global health issue with a high rate of death and morbidity, and
it is a cause of other diseases. In the early stages of chronic kidney disease, there are no evident
symptoms, therefore patients frequently fail to recognise the disease. Detection of CKD at an early
stage enables patients to obtain timely treatment to mitigate the disease’s progression. Due to their
rapid and precise recognition capabilities, machine learning models can aid doctors in achieving
this objective. In this paper, I suggest a technique for diagnosing CKD based on machine learning.
The CKD data set was taken from the machine learning repository at the University of California,
Irvine (UCI), which has a substantial number of missing values. Six machine learning algorithms
(logistic regression, random forest, support vector machine, k-nearest neighbour, naive Bayes clas-
sifier, and multilayer perceptron) were employed to create models after effectively completing the
missing data set. The best result among these machine learning models was achieved by random
forest with a diagnostic accuracy of 99 percent. By examining the errors produced by the existing
ommended method is 99 percent accurate. Also, the severity of CKD is anticipated based on the
individual’s clinical data.
models, I presented a model that combineslogistic regression with random forest. This rec-
