Department of IT and Computer Science, University of Maryland Global Campus, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(03), 453-475
Received on 28 October 2024; revised on 04 December 2024; accepted on 07 December 2024
Chronic Kidney Disease (CKD) is a progressive condition that affects millions globally, often leading to severe complications such as kidney failure and cardiovascular diseases. Early detection and personalized treatment plans are crucial for mitigating the progression of CKD and improving patient outcomes. Traditional methods of predicting CKD progression rely on clinical expertise and static risk assessment tools, which may not effectively leverage the wealth of patient data available today. Machine learning (ML) offers a data-driven approach to predict disease progression by analysing complex relationships within heterogeneous datasets, including laboratory results, demographic information, and comorbidities. ML models such as Random Forests, Gradient Boosting, and Support Vector Machines have demonstrated efficacy in predicting CKD progression. These algorithms excel in handling high-dimensional data and capturing nonlinear patterns, enabling accurate risk stratification and identification of key predictors. For example, ML models can analyse glomerular filtration rates (GFR), albumin levels, and other biomarkers to predict the likelihood of CKD progression or the onset of end-stage renal disease (ESRD). Additionally, these models facilitate personalized treatment recommendations by integrating patient-specific data, optimizing therapeutic interventions, and improving adherence to care protocols. However, challenges such as data quality, model interpretability, and ethical concerns regarding algorithmic bias must be addressed to ensure reliable and equitable deployment of ML solutions in clinical settings. This study explores the potential of ML in CKD progression modelling, highlighting case studies, model development, and validation techniques. It emphasizes the need for interdisciplinary collaboration to integrate ML-based tools into existing healthcare frameworks, ultimately enhancing CKD management and patient care.
CKD; Machine Learning; Disease Progression Modelling; Personalized Medicine; Random Forests; Gradient Boosting
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Foluke Ekundayo. Machine learning for chronic kidney disease progression modelling: Leveraging data science to optimize patient management. World Journal of Advanced Research and Reviews, 2024, 24(3), 453-475. Article DOI: https://doi.org/10.30574/wjarr.2024.24.3.3730