Chronic Kidney Disease (CKD) Detection Analysis Using Machine Learning

A chronic kidney disease (CKD) is one of the twenty most common causes of death worldwide, affecting around 10% of adults. Kidney disease damages the kidneys and disrupts their normal function. As CKD prevalence increases, it is increasingly important to have effective predictions for the early diagnosis of the disease. There are many risks associated with chronic kidney disease, including heart disease, breast cancer, urinary tract inflammation (UTI), and infertility. For medical experts, diagnosing CKD at a very early stage is very difficult. It is possible to detect CKD early by using computer-aided diagnostic methods. As a part of computer-aided diagnostics and medical applications, machine learning is essential for detecting diseases and its stages. This paper provides an investigation of various algorithms and methods used in detection of Chronic Kidney Disease and its stages. This survey also identifies research gaps and suggests future research directions.