A machine learning driven nomogram for predicting chronic kidney disease stages 3–5

• Published in: Scientific reports Vol. 13; no. 1; pp. 21613 - 14
• Main Authors: Ghosh, Samit Kumar, Khandoker, Ahsan H.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.12.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 692/4022/1585/104; 692/699/1585/104; Humanities and Social Sciences; Kidney diseases; Kidneys; Learning algorithms; Machine learning; multidisciplinary; Nomograms; Prediction models; Prevention; Regression analysis; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-48815-w

Chronic kidney disease (CKD) remains one of the most prominent global causes of mortality worldwide, necessitating accurate prediction models for early detection and prevention. In recent years, machine learning (ML) techniques have exhibited promising outcomes across various medical applications. This study introduces a novel ML-driven nomogram approach for early identification of individuals at risk for developing CKD stages 3–5. This retrospective study employed a comprehensive dataset comprised of clinical and laboratory variables from a large cohort of diagnosed CKD patients. Advanced ML algorithms, including feature selection and regression models, were applied to build a predictive model. Among 467 participants, 11.56% developed CKD stages 3–5 over a 9-year follow-up. Several factors, such as age, gender, medical history, and laboratory results, independently exhibited significant associations with CKD (p < 0.05) and were utilized to create a risk function. The Linear regression (LR)-based model achieved an impressive R-score (coefficient of determination) of 0.954079, while the support vector machine (SVM) achieved a slightly lower value. An LR-based nomogram was developed to facilitate the process of risk identification and management. The ML-driven nomogram demonstrated superior performance when compared to traditional prediction models, showcasing its potential as a valuable clinical tool for the early detection and prevention of CKD. Further studies should focus on refining the model and validating its performance in diverse populations.