Evidence map›Paper›PMID 35047155›Full record

ArticleJournal of healthcare engineering2022

Data Mining in Employee Healthcare Detection Using Intelligence Techniques for Industry Development.

Abolfazl Mehbodniya, Ihtiram Raza Khan, Sudeshna Chakraborty, M Karthik, Kamakshi Mehta, Liaqat Ali, Stephen Jeswinde Nuagah

RetractedAbstract readRetracted Publication
In one paragraph

Article in Journal of healthcare engineering, 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 It has been retracted, and should not be counted.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 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
–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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Hyperparameter optimization for cardiovascular disease data-driven prognostic system.Visual computing for industry, biomedicine, and art · 2023
    Article
  4. Article
  5. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Abolfazl MehbodniyaDepartment of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), Doha, Kuwait.ORCID 0000-0002-0945-512X
Ihtiram Raza KhanDepartment of Computer Science, Jamia Hamdard, Delhi, India.
Sudeshna ChakrabortyDepartment of Computer Science and Engineering, Lloyd Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India.
M KarthikDepartment of Electrical and Electronics Engineering, Kongu Engineering College, Erode, Tamil Nadu, India.ORCID 0000-0001-5355-9340
Kamakshi MehtaAmity College of Commerce, Amity University, Gurgaon, Haryana, India.
Liaqat AliDepartment of Electrical Engineering, University of Science and Technology Bannu, Bannu, Pakistan.ORCID 0000-0002-3095-7271
Stephen Jeswinde NuagahDepartment of Electrical Engineering, Tamale Technical University, Tamale, Ghana.ORCID 0000-0001-5687-9649

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEven in today's environment, when there is a plethora of information accessible, it may be difficult to make appropriate choices for one's well-being. Data mining, machine learning, and computational statistics are among the most popular arenas of training today, and they are all aimed at secondary empowered person in making good decisions that will maximize the outcome of whatever working area they are involved with. Because the degree of rise in the number of patient roles is directly related to the rate of people growth and lifestyle variations, the healthcare sector has a significant need for data processing services. When it comes to cancer, the prognosis is an expression that relates to the possibility of the patient surviving in general, but it may also be used to describe the severity of the sickness as it will present itself in the patient's future timeline.

resultsProviding a good contribution to the general health welfare of noncommercial potential workers in the healthcare sector is an opportunity provided by this recommended job outcome. It is anticipated that alternative solutions to these constraints, as well as automation of the whole process flow of all five phases, will be the key focus of the work to be carried out shortly. Predicting health status of employee in industry or information trends is made easier by these data patterns. The proposed classifier achieves the accuracy rate of 93.265%.

Indexed as

AlgorithmsData MiningDelivery of Health CareHumansIntelligenceMachine Learning

Identifiers

PMID35047155
PMCPMC8763559

What OpenQuestion holds

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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.