Advancements in Machine Learning for the Diagnosis of Chronic Kidney Disease

Abstract

Chronic Kidney Disease (CKD) constitutes a significant global health issue, precipitating damage to the kidneys and stripping many individuals of their most productive years. Alarmingly, 40% of those affected by CKD remain oblivious to their condition, a stark contrast to many other diseases where early detection is more common. Unlike other conditions, CKD eludes cure unless identified promptly in its nascent stages. This research emphasizes the collection of critical indicators such as blood pressure and diabetes status to ascertain the presence of CKD in individuals. It proposes the employment of advanced machine learning techniques, including Random Forest, XGBoost, and Support Vector Machines, aiming to enhance early detection and thereby mitigate the disease's impact. Utilizing a CKD dataset, this study endeavors to predict the likelihood of CKD in individuals, offering a proactive approach to tackle this formidable health challenge.