Data-Driven Identification of High-Risk Patients for CKD-Machine Learning Perspective

One of the most common illnesses that affect people on a broad scale is chronic kidney disease, or CKD, which is lethal since it does not manifest itself until a person's kidneys have sustained irreparable damage. The progression of CKD is linked to several serious side effects, such as an increased risk of different illnesses, anemia, hyperlipidemia, nerve damage, problems during pregnancy, and even total kidney failure. This illness claims the lives of millions of individuals each year. As no significant symptoms can be used as a baseline to diagnose the condition, diagnosing CKD is challenging. We have developed a machine-learning strategy to determine whether a patient has CKD. The application of machine learning techniques to CKD prediction has the potential to improve patient outcomes by facilitating earlier disease detection and more effective management of the condition. The logistic regression model and Random forest demonstrated the best performance and interpretability, making them useful tools for clinical practice. These findings need to be confirmed by additional studies in order to enhance how accurately machine learning systems predict CKD.