Healthcare Predictions: unmasking the strengths and weaknesses of AI models for vitamin D deficiency assessment.

Ahmad Al-Qerem, Ruzayn Quddoura, Abdulhakeem Issa, Asma Sbaih

Abstract read

In one paragraph

Article in Journal, genetic engineering & biotechnology, 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

4 authors.

Ahmad Al-QeremDepartment of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan; University of Business and Technology, Jeddah 21448, Saudi Arabia. Electronic address: asma_sbeih@paluniv.edu.ps.

Ruzayn QuddouraDepartment of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan.

Abdulhakeem IssaDepartment of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan.

Asma SbaihEngineering Department, Faculty of Engineering and Information Technology, Palestine Ahliya University, Bethlehem, Palestine.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluates eight machine-learning regression models for estimating serum vitamin D level as a support tool for vitamin D deficiency assessment. A cohort dataset of 100 individuals (dataset 132) was analyzed using Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN/MLP), Linear Regression (LR), Elastic Net (EN), Ridge Regression (RR), Lasso Regression (LSR), and RANSAC Regressor (RAN). Model performance was assessed over 30 repeated runs using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R

Indexed as

ExplainabilityHealthcare analyticsMachine learningRandom forestSupport vector machinevitamin D deficiency

Identifiers

PMID42309600

DOI10.1016/j.jgeb.2026.100695

PMCPMC13112383

What OpenQuestion holds

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LicenceCC BY

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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.