Evaluating Machine Learning Algorithms for Early Detection of Chronic Kidney Disease and Improved Cardiovascular Health Outcomes

Аннотация

CKD is a major public health problem affecting millions worldwide and is inextricably linked to adverse cardiovascular outcomes. Early detection is paramount to effective intervention and management; however, traditional methods usually lack the desired diagnostic performance. This review discusses the performance of different ML algorithms for the early detection of CKD: logistic regression, decision trees, random forests, support vector machines, neural networks, and gradient boosting machines. Each of the algorithms has been subsequently analyzed concerning data diversity, which includes electronic health records and clinical parameters, for performance, advantages, and limitations of the algorithms concerning CKD risk. Integrating ML into the healthcare system is one of the promising avenues that can bring about improvement in patient outcomes. However, there are several challenges to data quality, privacy, and clinician acceptance. Current research and case studies will illustrate how ML can be applied to improve early diagnosis for timely interventions that could improve the management of CKD and reduce cardiovascular risks. The following paper will summarize the current role of machine learning in changing the face of CKD detection for improved cardiovascular health outcomes. Key Words: Chronic Kidney Disease; Cardiovascular Health; Early Detection; Machine Learning; Healthcare Integration; Electronic Health Records