OBJECTIVES: Chronic kidney disease (CKD) is a growing concern in the US, with approximately 14% of the adults having kidney ailments. Since lack of proper healthcare facilities and inadequate awareness lead to a progression of the disease, it is of ardent need to identify the level of kidney dysfunction and undertake prompt treatment actions.

METHODS: Optum® de-identified Market Clarity database was used for this study, where Electronic Health Record (EHR) data was mainly exploited to accrue patients’ underlying comorbidities and laboratory test results, encompassing 30 predictors. A total of 130,000 patients, aged 45 years and older were considered from 2015-2021, with CKD patients categorized as mild (3.7%). moderate (13.8%) and severe (0.7%). Supervised ML techniques which include Logistic regression, Random Forest, Gradient Boosting, Neural Network and XG Boost were used to anticipate the disease stages.

RESULTS: The accuracy of disease severity was evaluated for each of the models to obtain the most precise results. XG Boost provided the best accuracy of 73% (AUC: 0.8 and F1 score: 0.4).

CONCLUSIONS: The vast repository of clinical data in the EHR database is effectively utilized in our study for appropriate prediction of CKD progression. This model can be implemented as a decision support tool to minimize prescription errors and assist the providers with timely and accurate prognosis, enabling an efficient care delivery for CKD patients.