Evidence map›Paper›PMID 35958813›Full record

ArticleBioMed research international2022

An Image Processing Approach for Detection of Prenatal Heart Disease.

Saravana Selvan, S John Justin Thangaraj, J Samson Isaac, T Benil, K Muthulakshmi, Hesham S Almoallim, Sulaiman Ali Alharbi, R R Kumar, Sojan Palukaran Thimothy

RetractedOpen access · hybridAbstract readRetracted Publication
PubMed Full text at PMC Publisher
In one paragraph

Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
5.6field-weighted citation impact, top 3% 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, 34 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 7 institutions in 5 countries.

Saravana SelvanFaculty of Engineering & Computer Technology, AIMST University, Bedong, Kedah 08100, Malaysia.
S John Justin ThangarajDepartment of Computer Science Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu 600124, India.
J Samson IsaacDepartment of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu 641114, India.
T BenilDepartment of Information Technology, M.Kumarasamy College of Engineering, Karur, Tamil Nadu 639113, India.
K MuthulakshmiDepartment of Information Technology, Panimalar Engineering College, Chennai, Tamil Nadu 600123, India.
Hesham S AlmoallimDepartment of Oral and Maxillofacial Surgery, College of Dentistry, King Saud University, PO Box-60169, Riyadh-11545, Saudi Arabia.
Sulaiman Ali AlharbiDepartment of Botany and Microbiology, College of Science, King Saud University, PO Box-2455, Riyadh-11451, Saudi Arabia.
R R KumarDepartment of Civil Engineering, University of Houston, Texas, USA.
Sojan Palukaran ThimothyFaculty of Mechanical Engineering, Arba Minch Institute of Technology (AMIT) Arba Minch University, Ethiopia.ORCID https://orcid.org/0000-0002-6765-8013
King Saud University · SAAIMST University · MYArba Minch University · ETKarunya University · INM. Kumarasamy College of EngineeringSaveetha University · INUniversity of Houston · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prenatal heart disease, generally known as cardiac problems (CHDs), is a group of ailments that damage the heartbeat and has recently now become top deaths worldwide. It connects a plethora of cardiovascular diseases risks to the urgent in need of accurate, trustworthy, and effective approaches for early recognition. Data preprocessing is a common method for evaluating big quantities of information in the medical business. To help clinicians forecast heart problems, investigators utilize a range of data mining algorithms to examine enormous volumes of intricate medical information. The system is predicated on classification models such as NB, KNN, DT, and RF algorithms, so it includes a variety of cardiac disease-related variables. It takes do with an entire dataset from the medical research database of patients with heart disease. The set has 300 instances and 75 attributes. Considering their relevance in establishing the usefulness of alternate approaches, only 15 of the 75 criteria are examined. The purpose of this research is to predict whether or not a person will develop cardiovascular disease. According to the statistics, naïve Bayes classifier has the highest overall accuracy.

Indexed as

Data MiningHeart DiseasesAlgorithmsBayes TheoremHumansImage Processing, Computer-Assisted

Identifiers

PMID35958813
PMCPMC9363204
OpenAlexW4289521478

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.