Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements.

ABSTRACT: Chronic kidney disease (CKD) is a global health concern with early detection playing a pivotal role in effective management. Machine learning models demonstrate promise in CKD detection, yet the impact on detection and classification using different sets of clinical features remains under-explored. In this study, we focus on CKD classification and creatinine prediction using three sets of features: at-home, monitoring, and laboratory. We employ artificial neural networks (ANNs) and random forests (RFs) on a dataset of 400 patients with 25 input features, which we divide into three feature sets. Using 10-fold cross-validation, we calculate metrics such as accuracy, true positive rate (TPR), true negative rate (TNR), and mean squared error. Our results reveal RF achieves superior accuracy (9

SUBMITTER: Metherall B 

PROVIDER: S-EPMC11799517 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature