Evidence map›Paper›PMID 34557257›Full record

SynthesisComputational and mathematical methods in medicine2021

A Systematic Literature Review on Particle Swarm Optimization Techniques for Medical Diseases Detection.

Sobia Pervaiz, Zia Ul-Qayyum, Waqas Haider Bangyal, Liang Gao, Jamil Ahmad

RetractedOpen access · hybridAbstract readSystematic ReviewRetracted Publication
In one paragraph

Synthesis in Computational and mathematical methods in medicine, 2021. 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 It has been retracted, and should not be counted.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 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
10.0field-weighted citation impact, top 1% 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

17 citing papers in PubMed, 116 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Computing Drug-Drug Similarity from Patient-Centric Data.Bioengineering (Basel, Switzerland) · 2023
    Article
  6. Article
  7. Article
  8. Article
  9. A Systematic Analysis of 3D Deformation of Aging Breasts Based on Artificial Neural Networks.International journal of environmental research and public health · 2022
    Article
  10. Article
  11. Review
  12. Article
  13. Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs.Computational and mathematical methods in medicine · 2022
    Article
  14. COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques.Computational and mathematical methods in medicine · 2022
    Article
  15. Article
  16. Article
  17. Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches.Computational and mathematical methods in medicine · 2021
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 5 institutions in 2 countries.

Sobia PervaizDepartment of Computer Science, Abasyn University Islamabad Campus, Islamabad, Pakistan.ORCID https://orcid.org/0000-0003-2030-6667
Zia Ul-QayyumAllama Iqbal Open University, Islamabad, Pakistan.
Waqas Haider BangyalDepartment of Computer Science, University of Gujrat, Pakistan.ORCID https://orcid.org/0000-0002-5797-4821
Liang GaoHuazhong University of Science and Technology (HUST), Wuhan, China.ORCID https://orcid.org/0000-0002-1485-0722
Jamil AhmadHazara University, Mansehra, KPK, Pakistan.
Abasyn University · PKAllama Iqbal Open University · PKHazara University · PKHuazhong University of Science and Technology · CNUniversity of Gujrat · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) is the domain of computer science that focuses on the development of machines that operate like humans. In the field of AI, medical disease detection is an instantly growing domain of research. In the past years, numerous endeavours have been made for the improvements of medical disease detection, because the errors and problems in medical disease detection cause serious wrong medical treatment. Meta-heuristic techniques have been frequently utilized for the detection of medical diseases and promise better accuracy of perception and prediction of diseases in the domain of biomedical. Particle Swarm Optimization (PSO) is a swarm-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm during the searching of their food source. Consequently, for the versatility of numerical experimentation, PSO has been mostly applied to address the diverse kinds of optimization problems. However, the PSO techniques are frequently adopted for the detection of diseases but there is still a gap in the comparative survey. This paper presents an insight into the diagnosis of medical diseases in health care using various PSO approaches. This study presents to deliver a systematic literature review of current PSO approaches for knowledge discovery in the field of disease detection. The systematic analysis discloses the potential research areas of PSO strategies as well as the research gaps, although, the main goal is to provide the directions for future enhancement and development in this area. This paper gives a systematic survey of this conceptual model for the advanced research, which has been explored in the specified literature to date. This review comprehends the fundamental concepts, theoretical foundations, and conventional application fields. It is predicted that our study will be beneficial for the researchers to review the PSO algorithms in-depth for disease detection. Several challenges that can be undertaken to move the field forward are discussed according to the current state of the PSO strategies in health care.

Indexed as

Artificial IntelligenceAlzheimer DiseaseBayes TheoremComputational BiologyDiagnosis, Computer-AssistedHumansImage Interpretation, Computer-AssistedMachine LearningNeural Networks, ComputerNonlinear DynamicsStochastic Processes

Identifiers

PMID34557257
PMCPMC8455185
OpenAlexW3200525399

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.