Chronic kidney disease prediction using machine learning techniques

Abstract Goal three of the UN’s Sustainable Development Goal is good health and well-being where it clearly emphasized that non-communicable diseases is emerging challenge. One of the objectives is to reduce premature mortality from non-communicable disease by third in 2030. Chronic kidney disease (CKD) is among the significant contributor to morbidity and mortality from non-communicable diseases that can affected 10–15% of the global population. Early and accurate detection of the stages of CKD is believed to be vital to minimize impacts of patient’s health complications such as hypertension, anemia (low blood count), mineral bone disorder, poor nutritional health, acid base abnormalities, and neurological complications with timely intervention through appropriate medications. Various researches have been carried out using machine learning techniques on the detection of CKD at the premature stage. Their focus was not mainly on the specific stages prediction. In this study, both binary and multi classification for stage prediction have been carried out. The prediction models used include Random Forest (RF), Support Vector Machine (SVM) and Decision Tree (DT). Analysis of variance and recursive feature elimination using cross validation have been applied for feature selection. Evaluation of the models was done using tenfold cross-validation. The results from the experiments indicated that RF based on recursive feature elimination with cross validation has better performance than SVM and DT.