[Retracted] Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives

Abstract

Chronic kidney disease (CKD) is a global health issue with a high rate of morbidity and mortality and a high rate of disease progression. Because there are no visible symptoms in the early stages of CKD, patients frequently go unnoticed. The early detection of CKD allows patients to receive timely treatment, slowing the disease’s progression. Due to its rapid recognition performance and accuracy, machine learning models can effectively assist physicians in achieving this goal. We propose a machine learning methodology for the CKD diagnosis in this paper. This information was completely anonymized. As a reference, the CRISP-DM® model (Cross industry standard process for data mining) was used. The data were processed in its entirety in the cloud on the Azure platform, where the sample data was unbalanced. Then the processes for exploration and analysis were carried out. According to what we have learned, the data were balanced using the SMOTE technique. Four matching algorithms were used after the data balancing was completed successfully. Artificial intelligence (AI) (logistic regression, decision forest, neural network, and jungle of decisions). The decision forest outperformed the other machine learning models with a score of 92%, indicating that the approach used in this study provides a good baseline for solutions in the production.

Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning

“…[20,[25][26][27][28][29] However, these studies mostly focused on CKD prevention or identification. [21,[30][31][32][33] For mortality prediction in ESRD patients, most studies have focused on populations with only kidney transplant therapy or hemodialysis. [34][35][36] Therefore, this study intends to construct a mortality prediction model with AI for pre-ESRD and ESRD patients.…”

Implementation of clinical diagnosis system for chronic kidney disease using deep learning algorithms

“…The National Renal Association categorizes CKD into stages depending on the occurrence of renal disorders and GFR, which is a marker of kidney function [12]. To lower the disease's high mortality rate, research should be widened and focused on the disease's early stages, diagnosing its risk group by laboratory testing, and preventing patients from progressing to the latter stages, such as dialysis, transplantation, or death [13].…”

Retracted: Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”