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Chronic Kidney Disease (CKD) is an increasingly prevalent condition affecting13% of the US population. The disease is often a silent condition, making itsdiagnosis challenging. Identifying CKD stages from standard office visitrecords can help in early detection of the disease and lead to timelyintervention. The dataset we use is highly imbalanced. We propose ahierarchical meta-classification method, aiming to stratify CKD by severitylevels, employing simple quantitative non-text features gathered from officevisit records, while addressing data imbalance. Our method effectivelystratifies CKD severity levels obtaining high average sensitivity, precisionand F-measure (~93%). We also conduct experiments in which the dimensionalityof the data is significantly reduced to include only the most salient features.Our results show that the good performance of our system is retained even whenusing the reduced feature sets, as well as under much reduced training sets,indicating that our method is stable and generalizable.

Assessing Chronic Kidney Disease from Office Visit Records Using Hierarchical Meta-Classification of an Imbalanced Dataset