Evidence map›Paper›PMID 27106582›Full record

ArticleJournal of medical systems2016

Hybrid EANN-EA System for the Primary Estimation of Cardiometabolic Risk.

Aleksandar Kupusinac, Edita Stokić, Ilija Kovaćevic

Abstract read
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In one paragraph

Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Aleksandar KupusinacFaculty of Technical Sciences, Department of Computing and Control Engineering, University of Novi Sad, Trg Dositeja Obradovica 6, 21000, Novi Sad, Serbia. sasak@uns.ac.rs.
Edita StokićMedical Faculty, Department of Endocrinology, Diabetes and Metabolic Disorders, University of Novi Sad, Hajduk Veljkova 1, 21000, Novi Sad, Serbia.
Ilija KovaćevicFaculty of Technical Sciences, Department of Fundamentals Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000, Novi Sad, Serbia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The most important part of the early prevention of atherosclerosis and cardiovascular diseases is the estimation of the cardiometabolic risk (CMR). The CMR estimation can be divided into two phases. The first phase is called primary estimation of CMR (PE-CMR) and includes solely diagnostic methods that are non-invasive, easily-obtained, and low-cost. Since cardiovascular diseases are among the main causes of death in the world, it would be significant for regional health strategies to develop an intelligent software system for PE-CMR that would save time and money by extracting the persons with potentially higher CMR and conducting complete tests only on them. The development of such a software system has few limitations - dataset can be very large, data can not be collected at the same time and the same place (eg. data can be collected at different health institutions) and data of some other region are not applicable since every population has own features. This paper presents a MATLAB solution for PE-CMR based on the ensemble of well-learned artificial neural networks guided by evolutionary algorithm or shortly EANN-EA system. Our solution is suitable for research of CMR in population of some region and its accuracy is above 90 %.

Indexed as

Cardiovascular DiseasesNeural Networks, ComputerAdolescentAdultAgedAlgorithmsDatasets as TopicHumansMiddle AgedRisk AssessmentYoung AdultArtificial neural networkCardiometabolic riskEvolutionary algorithmIntelligent healthcare

Identifiers

PMID27106582

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.