Evidence map›Paper›PMID 35909858›Full record

ArticleComputational intelligence and neuroscience2022

Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models.

Krishna Kumar, Narendra Kumar, Aman Kumar, Mazin Abed Mohammed, Alaa S Al-Waisy, Mustafa Musa Jaber, Neeraj Kumar Pandey, Rachna Shah, Gaurav Saini, Fatma Eid and 1 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational intelligence and neuroscience, 2022. 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
4.8field-weighted citation impact, top 5% of its field
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, 18 citations in OpenAlex.

  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

11 authors at 9 institutions in 3 countries.

Krishna KumarDepartment of Hydro and Renewable Energy, Indian Institute of Technology, Roorkee 247667, India.ORCID https://orcid.org/0000-0002-7196-0792
Narendra KumarSchool of Computing, DIT University, Dehradun 248009, Uttarakhand, India.
Aman KumarAcSIR-Academy of Scientific and Innovative Research, Ghaziabad 201002, India.ORCID https://orcid.org/0000-0002-4718-3903
Mazin Abed MohammedCollege of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq.ORCID https://orcid.org/0000-0001-9030-8102
Alaa S Al-WaisyComputer Technologies Engineering Department, Information Technology Collage, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq.
Mustafa Musa JaberDepartment of Computer Science, Dijlah University College, Baghdad, Iraq.
Neeraj Kumar PandeySchool of Computing, DIT University, Dehradun 248009, Uttarakhand, India.ORCID https://orcid.org/0000-0003-4948-8513
Rachna ShahDepartment of CSE, Indian Institute of Information Technology, Guwahati 781015, India.
Gaurav SainiIndian Institute of Engineering Science and Technology (IIEST), Shibpur, West Bengal 711103, India.
Fatma EidTechnology Management, College of Business, Stony Brook University, Stony Brook, NY, USA.ORCID https://orcid.org/0000-0002-3625-8746
Mohammed Nasser Al-AndoliComputer Science & Information Systems Department, Faculty of Science, Sa'adah University, Sa'adah, Yemen.ORCID https://orcid.org/0000-0001-6491-9938
Dehradun Institute of Technology University · INCentral Building Research Institute · INDijlah University College · IQImam Ja’afar Al-Sadiq UniversityIndian Institute of Engineering Science and Technology, Shibpur · INIndian Institute of Information Technology Guwahati · INIndian Institute of Technology Roorkee · INStony Brook University · USUniversity of Anbar · IQ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressure, high cholesterol, and smoking significantly increase the risk of heart disease. To estimate the risk of heart disease is a complex process because it depends on various input parameters. The linear and analytical models failed due to their assumptions and limited dataset. The existing studies have used medical data for classification purposes, which help to identify the exact condition of the patient, but no one has developed any correlation equation which can be directly used to identify the patients. In this paper, mathematical models have been developed using the medical database of patients suffering from heart disease. Curve fitting and artificial neural network (ANN) have been applied to model the condition of patients to find out whether the patient is suffering from heart disease or not. The developed curve fitting model can identify the cardiac patient with accuracy, having a coefficient of determination (

Indexed as

Heart DiseasesMachine LearningDatabases, FactualHumansModels, TheoreticalNeural Networks, Computer

Identifiers

PMID35909858
PMCPMC9329013
OpenAlexW4286009507

What OpenQuestion holds

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LicenceCC BY
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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.