Abstract:
Chronic Kidney Disease (CKD) or chronic renal
disease has become a major issue with a steady growth rate. A
person can only survive without kidneys for an average time of
18 days, which makes a huge demand for a kidney transplant
and Dialysis. It is important to have effective methods for early
prediction of CKD. Machine learning methods are effective in
CKD prediction. This work proposes a workflow to predict CKD
status based on clinical data, incorporating data prepossessing, a
missing value handling method with collaborative filtering and
attributes selection. Out of the 11 machine learning methods
considered, the extra tree classifier and random forest classifier
are shown to result in the highest accuracy and minimal bias to
the attributes. The research also considers the practical aspects
of data collection and highlights the importance of incorporating
domain knowledge when using machine learning for CKD status
prediction.
Citation:
I. U. Ekanayake and D. Herath, "Chronic Kidney Disease Prediction Using Machine Learning Methods," 2020 Moratuwa Engineering Research Conference (MERCon), 2020, pp. 260-265, doi: 10.1109/MERCon50084.2020.9185249.