An Analysis of Kidney Disease Prediction and Diagnosis using Machine Learning Strategies

With the advent of AI and machine learning (ML), clinical medicine has undergone a sea change, opening up new possibilities for advancements in both practice and research. The present state and future possibilities of using ML and Deep Learning (DL) to chronic kidney disease (CKD) are examined in this narrative review. Some examples that highlight the substantial importance of ML-diagnostic strategies are the rapid discovery of renal illness and the following prompt treatment of the condition. Predicting the onset of chronic kidney disease (CKD) using DL is an emerging area of research that seeks to equip doctors with AI-powered tools. A number of studies have attempted to compare and contrast various ML approaches in terms of their practicality, efficacy, and feasibility. One obvious limitation, though, has always been the absence of a comprehensive review of this material. So, this research familiarizes two contexts—one for ML-strategies, which classifies distinct essentials of kidney-disease-diagnosis(KDD), and another for medical sub-fields connected to DL—and provides a thorough literature evaluation of ML and DL utilizations in KDD. Also mentioned are areas where research is lacking and potential avenues for further investigation.