An Extreme Gradient Boosting-based Approach for Effective Chronic Kidney Disease Diagnosis
Chronic kidney disease is one of the critical illnesses that affects
roughly 10% of the people in the world. Early and accurate
prediction of such disease is required for proper treatment. The use
of machine learning (ML) for medical diagnosis in healthcare has
increased. The doctor can identify the disease early with the aid of
ML algorithms and approaches. This study aims to develop a
diagnosis approach to recognize chronic kidney disease and assist
the experts for exploring preventive measures early using extreme
gradient boosting (XGBoost) model. The XGBoost is used due to
its ability in-build features to manipulate missing data and its
regularization capability to handle unbalanced datasets. The
approach is trained and evaluated on a public dataset consisted of
24 features for 400 patients taken from the University of California
Irvine (UCI) repository. The mean and most frequent values are
used respectively for replacing the missing numerical and
categorical values. The experimental results using a 10-fold crossvalidation and holdout test techniques with a number of evaluation
metrics exposed that the XGBoost model of the proposed approach
achieves a competitive high result compared with the recent work
on the same dataset. It attained 99.9% of AUC mean for the 10-
fold cross-validation test and 99.6 of accuracy for 60% holdout
test from the dataset to diagnosis the chronic kidney disease.
Uterine Contractions (UC) and Fetal Heart Rate (FHR) are the most common techniques for evaluating fetal and maternal assessment during pregnancy and detecting the changes in fetal oxygenation…
Chronic kidney disease is one of the critical illnesses that affects
roughly 10% of the people in the world. Early and accurate
prediction of such disease is required for proper…