ArticleComputational intelligence and neuroscience2022
Analysis of Diabetes Clinical Data Based on Recurrent Neural Networks.
Article in Computational intelligence and neuroscience, 2022. 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 It has been retracted, and should not be counted.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 1 paper.
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
1 citing paper in PubMed.
- Retracted: Analysis of Diabetes Clinical Data Based on Recurrent Neural Networks.Computational intelligence and neuroscience · 2023Article
Corrections and comments
- Retraction · 2023-09-27Concerns/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· Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Informed/Patient Consent - None/Withdrawn · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Paper Mill · Computer-Aided Content or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 · · See also: https://pubpeer.com/publications/0D8301507AC77F0D924E9A900027A2
- Retracted
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
At present, diabetes is one of the most important chronic noncommunicable diseases, that have threatened human health. By 2020, the number of diabetic patients worldwide has reached 425 million. This amazing number has attracted the great attention of various countries. With the progress of computing technology, many mathematical models and intelligent algorithms have been applied in different fields of health care. 822 subjects were selected in this paper. They were divided into 389 diabetic patients and 423 nondiabetic patients. Each of the subjects included 41 indicators. Too many indicator variables would increase the computational effort and there could be a strong correlation and data redundancy between the data. Therefore, the sample features were first dimensionally reduced to generate seven new features in the new space, retaining up to 99.9% of the valid information from the original data. A diagnostic and classification model for diabetes clinical data based on recurrent neural networks were constructed, and particle swarm optimization (PSO) was introduced to optimise recurrent neural network's hyperparameters to achieve effective diagnosis and classification of diabetes.
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.