Chronic Kidney Disease Analysis Using Machine Learning

Chronic Kidney Disease (CKD) is a progressively developing medical condition affecting millions globally, commonly remaining asymptomatic until reaching advanced stages. Timely detection plays a crucial role in enhancing patient outcomes and reducing healthcare expenses. The utilization of machine learning (ML) techniques have emerged as a potent approach for identifying CKD through the analysis of clinical and biochemical data. This investigation delves into the utilization of supervised learning algorithms, encompassing Decision Trees, Support Vector Machines (SVM), Random Forests, and Neural Networks, to forecast CKD using datasets that include patient details like age, blood pressure, glucose levels, and creatinine values. The outlined strategy underscores the significance of feature selection, model refinement, and cross validation to ensure heightened accuracy, precision, and recall levels. Findings suggest that ML models exhibit considerable predictive accuracy, surpassing conventional statistical methods in the early detection of CKD. This study accentuates the potential of machine learning in serving as a diagnostic tool in the healthcare sector, facilitating prompt intervention and enhanced disease management