Evidence map›Paper›PMID 41623835›Full record

ArticleBioinformation2025

Use of machine learning to predict hypertension based on BMI and routine lab data.

Amisha Sood, Sangeeta Gupta, Shweta Ramnarayan Borkar, Amrit Podder, Parth Jani

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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.

Amisha SoodDepartment of Psychiatry, Wrexham Maelor Hospital, Wrexham.
Sangeeta GuptaDepartment of Physiology, AIIMS Gorakhpur, Uttar Pradesh, India.
Shweta Ramnarayan BorkarDepartment of Internal Medicine, Datta Meghe Medical College, Datta Meghe Institute of Higher Education and Research, Nagpur, India.
Amrit PodderDepartment of Physiology, Teerthanker Mahaveer Medical College & Research Centre, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India.
Parth JaniDepartment of General Medicine, All India Institute of Medical Sciences, Rajkot, Gujarat, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Random Forest machine learning model could be used to classify hypertension using BMI and regular laboratory characteristics. The overall number of participants included in the analysis was 150 and multiple models were trained on key clinical variables including fasting glucose, LDL and BMI. The Random Forest model revealed the best predictive accuracy of 88.0%. Thus, we show the possibility of applying non-invasive poor data to early detection of hypertension. According to the study, machine learning could increase the effectiveness of screening and patient outcomes.

Indexed as

BMIfasting glucosehypertensionLDLmachine learning

Identifiers

PMID41623835
PMCPMC12859290

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.