Evidence map›Paper›PMID 38811939›Full record

ArticleBMC medical informatics and decision making2024

An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health.

Belqes Alsadi, Saleh Musleh, Hamada R H Al-Absi, Mahmoud Refaee, Rizwan Qureshi, Nady El Hajj, Tanvir Alam

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Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

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2citing papers in PubMed, 1 pooled it
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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

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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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

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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.

Belqes AlsadiCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Saleh MuslehCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Hamada R H Al-AbsiCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Mahmoud RefaeeHamad Medical Corporation, Doha, Qatar.
Rizwan QureshiDepartment of Imaging Physics, MD Anderson Cancer Center, The University of Texas, Houston, USA.
Nady El HajjCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Tanvir AlamCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar. talam@hbku.edu.qa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetes is a chronic condition that can result in many long-term physiological, metabolic, and neurological complications. Therefore, early detection of diabetes would help to determine a proper diagnosis and treatment plan.

methodsIn this study, we employed machine learning (ML) based case-control study on a diabetic cohort size of 1000 participants form Qatar Biobank to predict diabetes using clinical and bone health indicators from Dual Energy X-ray Absorptiometry (DXA) machines. ML models were utilized to distinguish diabetes groups from non-diabetes controls. Recursive feature elimination (RFE) was leveraged to identify a subset of features to improve the performance of model. SHAP based analysis was used for the importance of features and support the explainability of the proposed model.

resultsEnsemble based models XGboost and RF achieved over 84% accuracy for detecting diabetes. After applying RFE, we selected only 20 features which improved the model accuracy to 87.2%. From a clinical standpoint, higher HDL-Cholesterol and Neutrophil levels were observed in the diabetic group, along with lower vitamin B12 and testosterone levels. Lower sodium levels were found in diabetics, potentially stemming from clinical factors including specific medications, hormonal imbalances, unmanaged diabetes. We believe Dapagliflozin prescriptions in Qatar were associated with decreased Gamma Glutamyltransferase and Aspartate Aminotransferase enzyme levels, confirming prior research. We observed that bone area, bone mineral content, and bone mineral density were slightly lower in the Diabetes group across almost all body parts, but the difference against the control group was not statistically significant except in T12, troch and trunk area. No significant negative impact of diabetes progression on bone health was observed over a period of 5-15 yrs in the cohort.

conclusionThis study recommends the inclusion of ML model which combines both DXA and clinical data for the early diagnosis of diabetes.

Indexed as

Absorptiometry, PhotonDiabetes Mellitus, Type 2Machine LearningAdultAgedBone DensityCase-Control StudiesFemaleHumansMaleMiddle AgedQatarBone healthDiabetesDual Energy X-ray AbsorptiometryMachine learningQatar Biobank (QBB)

Identifiers

PMID38811939
PMCPMC11134939

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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.