Early detection of chronic kidney disease using recursive feature elimination and cross-validated XGBoost model

Abstract

Chronic kidney disease (CKD) is listed among the top 20 leading causes of death worldwide, highlighting the urgent need for effective methods of early disease prediction for chronic conditions. This paper presents a novel strategy for the early diagnosis of CKD, leveraging machine learning (ML) methods to support researchers in their pursuit of preventative approaches. The study incorporates data from 400 patients, analyzing a comprehensive set of 25 attributes. The missing values in the dataset handled by mean and mode statistics were employed for numerical and nominal dimensions respectively. The recursive feature elimination (RFE) with cross-validation (CV) technique was utilized to identify the most crucial attributes. The resulting predictive model achieved positive outcomes in terms of accuracy, precision, recall, and [Formula: see text]-measure across all implemented classification algorithms (random forest (RF), Naive Bayes (NB), decision tree (DT), multi-layer neural network (MLNN), partial decision tree algorithm (PART), repeated incremental pruning to produce error reduction (RIPPER), [Formula: see text] Star and extreme gradient boosting (XGBoost)). Notably, the XGBoost classification algorithm demonstrated an impressive accuracy of up to 99.5%. The utilization of predictive models is indispensable for healthcare professionals, as they play a pivotal role in enabling early detection of CKD and mitigating the risk of kidney failure occurrence. This research offers valuable insights into the development of reliable methods for the early identification of CKD, providing a promising avenue for enhancing patient outcomes and reducing the burden of this prevalent chronic condition.