Evidence map›Paper›PMID 42291551›Full record

ArticleFrontiers in cardiovascular medicine2026

Predictive voting model for early diagnosis of diabetes dataset.

Talaa Al-Bimani, Jamil AlShaqsi, Rami Alkhawaldeh, Adil S Al Busaidi, Ali Tarhini

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Talaa Al-BimaniInformation Systems Department, Sultan Qaboos University, Muscat, Oman.
Jamil AlShaqsiInformation Systems Department, Sultan Qaboos University, Muscat, Oman.
Rami AlkhawaldehComputer Information Systems Department, The University of Jordan, Aqaba, Jordan.
Adil S Al BusaidiInnovation and Technology Transfer Center, Sultan Qaboos University, Muscat, Oman.
Ali TarhiniInformation Systems Department, Sultan Qaboos University, Muscat, Oman.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research aimed to construct predictive voting models (hard vote and soft vote) to improve the diabetes diagnosis system at the initial pre-diabetes stage using several risk factors retrieved from a dataset collected from a government hospital in Oman. The study focused on identifying significant predictors of diabetes and enhancing the accuracy of early diagnosis. The Knowledge Discovery in Database (KDD) model was utilized to conduct the experiments. A 33-month historical dataset comprising

Indexed as

classificationdata miningdiabetes in Omanknowledge discovery in database (KDD)prediction algorithmsvoting ensemble algorithms

Identifiers

PMID42291551
PMCPMC13253679

What OpenQuestion holds

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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.