Utilizing support vector machines for predictive analytics in chronic kidney diseases

Chronic kidney disease (CKD), due to rising patients, the high probability of deterioration towards end-stage renal
disease and inaccurate estimates of morbidity and mortality, constitutes a heavy burden on the sanitary infrastructure.
The aim of this research is to build a model for machine-learning, which uses comorbidity and data on drugs and
predicts population prevalence. Predictive health care prediction using machine learning is a daunting activity to help
clinicians assess the precise therapies for life-saving. In this paper, the study applies machine learning method in
combination with ensemble learning for estimation of chronic kidney disease with clinical evidence. They are based on
chronic kidney disease datasets and the efficiency of these models is compared to choose the best classifier for chronic
kidney disease prediction. The comparative analysis is estimated in terms of various metrics like classification
accuracy, f-measure, percentage error, etc. The results of simulation shows that the proposed ensemble machine
learning classifier namely Ensemble Support Vector Machine predicts well the chronic kidney disease from the
datasets than other existing ensemble methods