Machine Learning Approaches for Chronic Kidney Disease Prediction

Chronic Kidney Disease (CKD) is a major public health challenge worldwide, marked by a slow, progressive decline in renal functionality that frequently remains undetected until it reaches an advanced stage. Timely identification is critical for halting disease progression and preventing life-threatening complications. As healthcare systems accumulate increasingly large volumes of structured patient data, computational intelligence methods have become valuable tools for extracting clinically meaningful patterns from such records. This survey presents a systematic review of machine learning strategies applied to CKD detection, encompassing classical statistical classifiers, advanced ensemble frameworks, and deep learning architectures. The study highlights the significance of data preparation, dimensionality reduction, and multi-metric model evaluation. The overarching aim is to demonstrate how data-driven methods can complement clinical judgment, support early intervention, facilitate disease prevention, and contribute to more effective long-term disease management.