Evidence map›Paper›PMID 26090333›Full record

ArticleCardiovascular diagnosis and therapy2015

Artificial neural network modeling using clinical and knowledge independent variables predicts salt intake reduction behavior.

Hussain A Isma'eel, George E Sakr, Mohamad M Almedawar, Jihan Fathallah, Torkom Garabedian, Savo Bou Zein Eddine, Lara Nasreddine, Imad H Elhajj

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Article in Cardiovascular diagnosis and therapy, 2015. 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
0.5field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

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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, 5 citations in OpenAlex.

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

8 authors at 1 institution in 1 country.

Hussain A Isma'eel1 Division of Cardiology, Department of Internal Medicine, American University of Beirut, Beirut, Lebanon ; 2 Vascular Medicine Program, American University of Beirut Medical Center, Beirut, Lebanon ; 3 Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA ; 4 Department of Electrical & Computer Engineering, 5 Department of Nutrition & Food Sciences, American University of Beirut, Beirut, Lebanon.
George E Sakr1 Division of Cardiology, Department of Internal Medicine, American University of Beirut, Beirut, Lebanon ; 2 Vascular Medicine Program, American University of Beirut Medical Center, Beirut, Lebanon ; 3 Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA ; 4 Department of Electrical & Computer Engineering, 5 Department of Nutrition & Food Sciences, American University of Beirut, Beirut, Lebanon.
Mohamad M Almedawar1 Division of Cardiology, Department of Internal Medicine, American University of Beirut, Beirut, Lebanon ; 2 Vascular Medicine Program, American University of Beirut Medical Center, Beirut, Lebanon ; 3 Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA ; 4 Department of Electrical & Computer Engineering, 5 Department of Nutrition & Food Sciences, American University of Beirut, Beirut, Lebanon.
Jihan Fathallah1 Division of Cardiology, Department of Internal Medicine, American University of Beirut, Beirut, Lebanon ; 2 Vascular Medicine Program, American University of Beirut Medical Center, Beirut, Lebanon ; 3 Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA ; 4 Department of Electrical & Computer Engineering, 5 Department of Nutrition & Food Sciences, American University of Beirut, Beirut, Lebanon.
Torkom Garabedian1 Division of Cardiology, Department of Internal Medicine, American University of Beirut, Beirut, Lebanon ; 2 Vascular Medicine Program, American University of Beirut Medical Center, Beirut, Lebanon ; 3 Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA ; 4 Department of Electrical & Computer Engineering, 5 Department of Nutrition & Food Sciences, American University of Beirut, Beirut, Lebanon.
Savo Bou Zein Eddine1 Division of Cardiology, Department of Internal Medicine, American University of Beirut, Beirut, Lebanon ; 2 Vascular Medicine Program, American University of Beirut Medical Center, Beirut, Lebanon ; 3 Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA ; 4 Department of Electrical & Computer Engineering, 5 Department of Nutrition & Food Sciences, American University of Beirut, Beirut, Lebanon.
Lara Nasreddine1 Division of Cardiology, Department of Internal Medicine, American University of Beirut, Beirut, Lebanon ; 2 Vascular Medicine Program, American University of Beirut Medical Center, Beirut, Lebanon ; 3 Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA ; 4 Department of Electrical & Computer Engineering, 5 Department of Nutrition & Food Sciences, American University of Beirut, Beirut, Lebanon.
Imad H Elhajj1 Division of Cardiology, Department of Internal Medicine, American University of Beirut, Beirut, Lebanon ; 2 Vascular Medicine Program, American University of Beirut Medical Center, Beirut, Lebanon ; 3 Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA ; 4 Department of Electrical & Computer Engineering, 5 Department of Nutrition & Food Sciences, American University of Beirut, Beirut, Lebanon.
American University of Beirut Medical Center · LB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigh dietary salt intake is directly linked to hypertension and cardiovascular diseases (CVDs). Predicting behaviors regarding salt intake habits is vital to guide interventions and increase their effectiveness. We aim to compare the accuracy of an artificial neural network (ANN) based tool that predicts behavior from key knowledge questions along with clinical data in a high cardiovascular risk cohort relative to the least square models (LSM) method.

methodsWe collected knowledge, attitude and behavior data on 115 patients. A behavior score was calculated to classify patients' behavior towards reducing salt intake. Accuracy comparison between ANN and regression analysis was calculated using the bootstrap technique with 200 iterations.

resultsStarting from a 69-item questionnaire, a reduced model was developed and included eight knowledge items found to result in the highest accuracy of 62% CI (58-67%). The best prediction accuracy in the full and reduced models was attained by ANN at 66% and 62%, respectively, compared to full and reduced LSM at 40% and 34%, respectively. The average relative increase in accuracy over all in the full and reduced models is 82% and 102%, respectively.

conclusionsUsing ANN modeling, we can predict salt reduction behaviors with 66% accuracy. The statistical model has been implemented in an online calculator and can be used in clinics to estimate the patient's behavior. This will help implementation in future research to further prove clinical utility of this tool to guide therapeutic salt reduction interventions in high cardiovascular risk individuals.

Indexed as

artificial neural networks (ANNs)behavior predictiondietarydietary interventionsalt intake reductionSodium chloride

Identifiers

PMID26090333
PMCPMC4451318
OpenAlexW2198085028

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.