MACHINE LEARNING FOR CHRONIC KIDNEY DISEASE: ADVANCES, CHALLENGES, AND FUTURE RESEARCH DIRECTIONS

Abstract

Millions of individuals worldwide are affected by chronic kidney disease (CKD), a serious public health issue. To stop CKD from progressing to end-stage renal disease (ESRD), early identification and effective care are essential. However, conventional approaches to CKD diagnosis and prognosis are frequently insufficient, resulting in postponed therapy and subpar results. In the field of medicine, especially the diagnosis and prediction of CKD, machine learning (ML) has emerged as a potential approach. Large volumes of data can be analyzed by ML algorithms, which can also identify patterns that may elude human specialists. This may lead to more accurate CKD diagnosis and prognosis calculations, as well as the creation of novel, strong therapeutic approaches. In this article, we examine recent advances in machine learning (ML) in the field of chronic kidney disease (CKD), including research that employed ML to predict treatment outcomes and make diagnoses and prognoses. We also go over the difficulties and restrictions associated with using ML in the study of CKD, as well as potential future lines of inquiry. Our analysis highlights the potential of ML to improve CKD diagnosis, prognosis, and treatment, while also underscoring the need for further research to realize this potential fully