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Healthcare (Healthcare)

Publisher :

MDPI

Scopus Profile
Peer reviewed only
Scopus Profile
Open Access
  • medicine
  • health care research
  • Health assessment

    • +5

e-ISSN :

2227-9032

Issue Frequency :

Quarterly

Impact Factor :

2.4

Est. Year :

2013

Mobile :

41616837734

DOI :

YES

Country :

Switzerland

Language :

English

APC :

YES

Impact Factor Assignee :

Google Scholar

Email :

healthcare@mdpi.com, jr-healthcare@mdpi.com

Journal Descriptions

Healthcare (ISSN 2227-9032) is an international, peer-reviewed, open access journal (free for readers), which publishes original theoretical and empirical work in the interdisciplinary area of all aspects of medicine and health care research. Healthcare publishes Original Research Articles, Reviews, Case Reports, Research Notes and Short Communications. We encourage researchers to publish their experimental and theoretical results in as much detail as possible. For theoretical papers, full details of proofs must be provided so that the results can be checked; for experimental papers, full experimental details must be provided so that the results can be reproduced. Additionally, electronic files or software regarding the full details of the calculations, experimental procedure, etc., can be deposited along with the publication as “Supplementary Material”.


Healthcare (Healthcare) is :

International, Peer-Reviewed, Open Access, Refereed, medicine, health care research, Health assessment, Disease prevention, Diagnosis, Health policy, Nursing, Health Information Management , Online Quarterly Journal

UGC Approved, ISSN Approved: P-ISSN , E-ISSN - 2227-9032, Established in - 2013, Impact Factor - 2.4

Provide Crossref DOI

Indexed in Scopus, WoS, PubMed

Not indexed in DOAJ, UGC CARE

Indexing

Scopus Profile
Scopus
Scopus Profile
WoS
Scopus Profile
PubMed

+6 More

Publications of Healthcare

  • dott image June, 2023

Ensemble Learning for Disease Prediction: A Review

Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve ...

  • dott image December, 2022

Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccinatio...

Good vaccine safety and reliability are essential for successfully countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been re...

  • dott image December, 2022

Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccinatio...

Good vaccine safety and reliability are essential for successfully countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been re...

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