Previously submitted to: JMIR Research Protocols (no longer under consideration since Sep 19, 2023)
Date Submitted: Sep 3, 2022
Open Peer Review Period: Sep 3, 2022 - Oct 29, 2022
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
A Systematic Review and Meta-Analysis Protocol for Machine Learning Algorithms for Prediabetes Risk Calculation
ABSTRACT
Identifying and delivering interventions to patients with prediabetes was one strategy for dealing with the rising prevalence of T2DM. Risk assessment tools help in disease detection by allowing screening of the high risk group. Machine learning was also used to support in the detection and diagnosis of prediabetes. The purpose of this review is to assess the diagnostic test accuracy of various machine learning algorithms for calculating prediabetes risk. This protocol was written in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis of Protocols (PRISMA-P) statement. The databases that will be used include PubMed, ProQuest, and EBSCO, with access limited to January 1999 and September 2022 in English only. Two reviewers will identify articles independently by reading the titles, abstracts, and full-text articles. Any disagreement will be resolved through consensus. To assess the quality and potential for bias, the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool will be used. Data extraction and content analysis will be carried out in a systematic manner. A forest plot with 95% confidence intervals will be used to visualize quantitative data. The summary receiver operating characteristic curve will describe the diagnostic test outcome. The Review Manager 5.3 (Rev Man 5.3) software package will be used to analyze the data. Discussion: Using the proposed systematic review and meta-analysis, we will determine the diagnostic accuracy of various machine learning algorithms for estimating prediabetes risk. Machine learning classification is a form of artificial intelligence (AI) that allows computers to learn without being specifically programmed. It has been used to develop a scoring method for prediabetes identification and diagnosis. As far as we know, there is no systematic review and meta-analysis regarding machine learning utilization for prediabetes risk estimation. Therefore, we proposed this study to obtain the diagnostic accuracy of machine learning algorithms in estimating prediabetes risk. This protocol has been registered in the Prospective Registry of Systematic Review (PROSPERO) database. The registration number is CRD42021251242.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.