The article "Application of Machine Learning in Chronic Kidney Disease: Current Status and Future Prospects" by Charlotte Delrue, Sander De Bruyne, and Marijn M. Speeckaert explores the integration of machine learning (ML) in the field of chronic kidney disease (CKD). ML, a combination of statistics and computer science, has revolutionized clinical medicine by enabling the extraction of insights from large datasets, enhancing data interpretation, and improving clinical algorithms. The authors discuss the standard workflow for developing ML models, including data preprocessing, model training, validation, and testing, emphasizing the importance of clean and comprehensive data. They highlight the effectiveness of various ML algorithms such as linear regression, logistic regression, decision trees, random forests, k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) in CKD research and clinical practice. The article also reviews the current status of ML in CKD, noting its applications in diabetic nephropathy (DKD), IgA nephropathy (IgAN), and hemodialysis. ML has shown promise in improving the diagnosis, prognosis, and management of CKD, particularly in predicting disease progression and patient outcomes. For example, ML models have been used to predict kidney failure in DKD patients, with high accuracy and sensitivity. In IgAN, ML algorithms have enhanced the prediction of kidney function decline and provided valuable insights for clinical decision-making. In hemodialysis, ML has been applied to optimize dialysis prescription, manage complications, and predict patient mortality. Looking ahead, the authors emphasize the potential for collaborative efforts between nephrologists and AI researchers to develop extensive databases and efficient models for CKD diagnosis and treatment. They highlight the importance of ethical considerations, such as data privacy and patient consent, and the need for continuous learning and maintenance of ML models. The article concludes by discussing the future prospects of ML in CKD, including the potential for non-invasive diagnoses and the integration of ML into clinical workflows to enhance patient care and reduce healthcare costs.The article "Application of Machine Learning in Chronic Kidney Disease: Current Status and Future Prospects" by Charlotte Delrue, Sander De Bruyne, and Marijn M. Speeckaert explores the integration of machine learning (ML) in the field of chronic kidney disease (CKD). ML, a combination of statistics and computer science, has revolutionized clinical medicine by enabling the extraction of insights from large datasets, enhancing data interpretation, and improving clinical algorithms. The authors discuss the standard workflow for developing ML models, including data preprocessing, model training, validation, and testing, emphasizing the importance of clean and comprehensive data. They highlight the effectiveness of various ML algorithms such as linear regression, logistic regression, decision trees, random forests, k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) in CKD research and clinical practice. The article also reviews the current status of ML in CKD, noting its applications in diabetic nephropathy (DKD), IgA nephropathy (IgAN), and hemodialysis. ML has shown promise in improving the diagnosis, prognosis, and management of CKD, particularly in predicting disease progression and patient outcomes. For example, ML models have been used to predict kidney failure in DKD patients, with high accuracy and sensitivity. In IgAN, ML algorithms have enhanced the prediction of kidney function decline and provided valuable insights for clinical decision-making. In hemodialysis, ML has been applied to optimize dialysis prescription, manage complications, and predict patient mortality. Looking ahead, the authors emphasize the potential for collaborative efforts between nephrologists and AI researchers to develop extensive databases and efficient models for CKD diagnosis and treatment. They highlight the importance of ethical considerations, such as data privacy and patient consent, and the need for continuous learning and maintenance of ML models. The article concludes by discussing the future prospects of ML in CKD, including the potential for non-invasive diagnoses and the integration of ML into clinical workflows to enhance patient care and reduce healthcare costs.