ArticleComputational intelligence and neuroscience2021
Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Easy-to-use machine learning system for the prediction of IDH mutation and 1p/19q codeletion using MRI images of adult-type diffuse gliomas.Brain tumor pathology · 2023Article
- Retracted: Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives.Computational intelligence and neuroscience · 2023Article
Corrections and comments
- Retraction · 2023-10-04Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Informed/Patient Consent - None/Withdrawn · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.