Using Machine Learning to Refer Patients with Chronic Kidney Disease to Secondary Care

There has been growing interest recently in using machine learning techniques as an aid in clinical medicine. Machine learning offers a range of classification algorithms which can be applied to medical data to aid in making clinical predictions. Recent studies have demonstrated the high predictive accuracy of various classification algorithms applied to clinical data. Several studies have already been conducted in diagnosing or predicting chronic kidney disease at various stages using different sets of variables. In this study we are investigating the use of machine learning techniques with blood test data. Such a system could aid renal teams in making recommendations to primary care general practitioners to refer patients to secondary care where patients may benefit from earlier specialist assessment and medical intervention. We are able to achieve an overall accuracy of 88.48% using logistic regression, 87.12% using ANN and 85.29% using SVM. ANNs performed with the highest sensitivity at 89.74% compared to 86.67% for logistic regression and 85.51% for SVM.