Analysis of Chronic Kidney Disease Using Machine Learning Algorithms: A Survey

Authors

  • Swapnaja Ubale, Vrushali Uttarwar, Deepali Ujalambkar, Rahul Bhole, Navnath Kale

Abstract

Kidney illness is a serious problem in public health that has only recently come to light. The term "chronic kidney disease" (CKD) is widely used to describe a progressive decline in kidney function over time because of various disorders. Many people get sick abruptly and without realizing it because of a variety of risk factors, include food, the atmosphere, and living conditions. Chronic kidney illness diagnosis is often invasive, expensive, time-consuming, and fraught with danger. Particularly in low-income countries, this means that many people with treatable diseases go undiagnosed and untreated for too long. Doctors need to have the ability to recognize the signs of this illness early on if they want to preserve their patients' lives. Kidney disease (CKD) can be diagnosed and treated early to prevent further damage to the kidneys and decrease the disease's course. The researchers utilized a wide range of techniques to detect this disease at an early stage. This study's objective is to investigate and acquire a deeper comprehension of the several approaches that are utilized in the renal disease prognostication process. It is possible to classify chronic kidney disease (CKD) using techniques like Artificial Neural Networks (ANN), Naive Bayes, Support Vector Machines (SVM), ada boost classifiers, Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), MBPN, and others. To mine data from the medical industry, numerous tools are employed to find large datasets in kidney disease.

Published

2023-02-22

How to Cite

Swapnaja Ubale, Vrushali Uttarwar, Deepali Ujalambkar, Rahul Bhole, Navnath Kale. (2023). Analysis of Chronic Kidney Disease Using Machine Learning Algorithms: A Survey. SJIS-P, 35(1), 108–114. Retrieved from http://sjis.scandinavian-iris.org/index.php/sjis/article/view/242

Issue

Section

Articles