Unlocking the Knowledge of Patient Similarities in Chronic Kidney Disease CKD Using Machine Learning with Prospects of Federal Learning

Chronic kidney disease (CKD) is a global public health issue characterized by progressive loss of renal function, significant morbidity, and associations with cardiovascular disease and end-stage renal disease (ESRD). Common causes include diabetes, hypertension, and glomerulonephritis, yet CKD remains largely underdiagnosed. This study addresses the challenge of improving CKD detection by leveraging advanced machine learning (ML) techniques. Convolutional neural networks (CNN) are employed to automatically extract features and classify CKD, while K-nearest neighbors (KNN) predicts outcomes based on patient similarities. The dataset integrates symptoms, lifestyle factors, and consultation records to enhance prediction accuracy. Furthermore, federated learning (FL) is proposed as a privacy-preserving framework to enable collaborative model training across institutions, leveraging diverse datasets while ensuring data confidentiality. By combining robust ML algorithms with FL, this research aims to provide a scalable and secure approach to early CKD diagnosis, enhancing clinical decision-making and public health outcomes.