Evidence map›Paper›PMID 38439210›Full record

ArticleJournal of diabetes investigation2024

Performance of different machine learning algorithms in identifying undiagnosed diabetes based on nonlaboratory parameters and the influence of muscle strength: A cross-sectional study.

Ying Xu, Shanhu Qiu, Jinli Ye, Dan Chen, Donglei Wang, Xiaoying Zhou, Zilin Sun

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes investigation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 27% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 1 country.

Ying XuDepartment of Endocrine Metabolism, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.ORCID https://orcid.org/0009-0008-4182-6817
Shanhu QiuDepartment of General Practice, School of Medicine, Institute of Diabetes, Zhongda Hospital, Southeast University, Nanjing, China.
Jinli YeSchool of Mathematics and Statistics, Yunnan University, Kunming, China.
Dan ChenSchool of Mathematics and Statistics, Yunnan University, Kunming, China.
Donglei WangDepartment of Endocrinology, School of Medicine, Institute of Diabetes, Zhongda Hospital, Southeast University, Nanjing, China.
Xiaoying ZhouDepartment of Endocrinology, School of Medicine, Institute of Diabetes, Zhongda Hospital, Southeast University, Nanjing, China.
Zilin SunDepartment of Endocrinology, School of Medicine, Institute of Diabetes, Zhongda Hospital, Southeast University, Nanjing, China.
Zhongda Hospital Southeast University · CNYunnan University · CNKunming University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

AIMS/

introductionMachine learning algorithms based on the artificial neural network (ANN), support vector machine, naive Bayesian or logistic regression model are commonly used to identify diabetes. This study investigated which approach performed the best and whether muscle strength provided any incremental benefit in identifying undiagnosed diabetes in Chinese adults.

methodsThis cross-sectional study enrolled 4,482 eligible participants from eight provinces in China, who were randomly divided into the training dataset (n = 3,586) and the testing dataset (n = 896). Muscle strength was assessed by handgrip strength and the number of chair stands in the 30-s chair stand test. An oral glucose tolerance test was used to ascertain undiagnosed diabetes. The areas under the curve (AUCs) were calculated accordingly and compared with each other.

resultsOf the included participants, 233 had newly diagnosed diabetes. All the four machine learning algorithms, which were developed based on nonlaboratory parameters, showed acceptable discriminative ability in identifying undiagnosed diabetes (all AUCs >0.70), with the ANN approach performing the best (AUC 0.806). Adding handgrip strength or the 30-s chair stand test to this approach did not increase the AUC further (P = 0.39 and 0.26, respectively). Furthermore, compared with the New Chinese Diabetes Risk Score, the ANN approach showed a larger AUC in identifying undiagnosed diabetes (P

conclusionsThe ANN approach performed the best in identifying undiagnosed diabetes in Chinese adults; however, the addition of muscle strength might not improve its efficacy.

Indexed as

Diabetes MellitusMachine LearningMuscle StrengthAdultAgedAlgorithmsChinaCross-Sectional StudiesFemaleGlucose Tolerance TestHand StrengthHumansMaleMiddle AgedNeural Networks, ComputerDiabetesMachine learningMuscle strength

Identifiers

PMID38439210
PMCPMC11143412
OpenAlexW4392465967

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.