Evidence map›Paper›PMID 37646158›Full record

ArticleEpidemiology and infection2023

COVID-19 cluster identification and support vector machine classifier model construction using global healthcare and socio-economic features.

Soumya Kanti Guha, Sandip Sadhukhan, Sougata Niyogi

Abstract read
In one paragraph

Article in Epidemiology and infection, 2023. 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

3 authors.

Soumya Kanti GuhaDepartment of Computer Application, Dinabandhu Andrews Institute of Technology and Management, Kolkata, India.
Sandip SadhukhanDepartment of Computer Application, Dinabandhu Andrews Institute of Technology and Management, Kolkata, India.
Sougata NiyogiDepartment of Medical Lab Technology, Dinabandhu Andrews Institute of Technology and Management, Kolkata, India.ORCID 0000-0002-5257-9339

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronaviruses of the human variety have been the culprit of global epidemics of varying levels of lethality, including COVID-19, which has impacted more than 200 countries and resulted in 5.7 million fatalities as of May 2022. Effective clinical management necessitates the allocation of sufficient resources and the employment of appropriately skilled personnel. The elderly population and individuals with diabetes are at increased risk of more severe manifestations of COVID-19. Countries with a higher gross domestic product (GDP) typically exhibit superior health outcomes and reduced mortality rates. Here, we suggest a predictive model for the density of medical doctors and nursing personnel for 134 countries using a support vector machine (SVM). The model was trained in 107 countries and tested in 27, with promising results shown by the kappa statistics and ROC analysis. The SVM model used for predictions showed promising results with a high level of agreement between actual and predicted cluster values.

Indexed as

COVID-19AgedDelivery of Health CareHumansROC CurveSocioeconomic FactorsSupport Vector Machineclinical managementcoronaviruseshealthcare outcomesmortality ratespredictive modellingsupport vector machine

Identifiers

PMID37646158
PMCPMC10600736

What OpenQuestion holds

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LicenceCC BY
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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.