Evidence map›Paper›PMID 35845582›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2022

An Improved Machine Learning Model for Diagnostic Cancer Recognition Using Artificial Intelligence.

N Arivazhagan, J Venkatesh, K Somasundaram, K Vijayalakshmi, S Sathiya Priya, M Suresh Thangakrishnan, K Senthamilselvan, B Lakshmi Dhevi, D Vijendra Babu, S Chandragandhi and 1 more

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

Article in Evidence-based complementary and alternative medicine : eCAM, 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 It has been retracted, and should not be counted.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
1.3field-weighted citation impact, top 19% 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, 16 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 7 institutions in 2 countries.

N ArivazhaganDepartment of Computational Intelligence, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur 603203, India.ORCID https://orcid.org/0000-0001-9515-4237
J VenkateshDepartment of Computer Science and Engineering, Chennai Institute of Technology, Kundrathur, Chennai 600069, Tamilnadu, India.
K SomasundaramInstitute of Information of Technology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, Tamilnadu, India.ORCID https://orcid.org/0000-0002-0429-7759
K VijayalakshmiDepartment of CSE, Ramco Institute of Technology, Rajapalayam, Tamilnadu, India.
S Sathiya PriyaDepartment of Electronics and Communication Engineering, Panimalar Institute of Technology, Chennai, Tamilnadu, India.
M Suresh ThangakrishnanDepartment of Computer Science and Engineering, Einstein College of Engineering, Tirunelveli 627012, Tamilnadu, India.
K SenthamilselvanDepartment of Electronics and communication Engineering, Prince Shri Venkateshwara Padmavathy Engineering College, Ponmar, Chennai, Tamilnadu, India.
B Lakshmi DheviInstitute of Artificial Intelligence and Machine Learning, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, Tamilnadu, India.
D Vijendra BabuDepartment of Electronics and Communication Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission's Research Foundation, Chennai, Tamilnadu, India.ORCID https://orcid.org/0000-0001-9925-979X
S ChandragandhiAP/CSE, JCT College of Engineering and Technology, Pichanur, Tamilnadu, India.ORCID https://orcid.org/0000-0002-3874-0149
Fekadu Ashine ChamatoDepartment of Chemical Engineering, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.ORCID https://orcid.org/0000-0002-6475-7075
Saveetha University · INAddis Ababa Science and Technology University · ETJCT College Of Engineering And TechnologyShri Venkateshwara University · INSRM Institute of Science and Technology · INTirunelveli Medical College · INVinayaka Missions University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the medical field, some specialized applications are currently being used to treat various ailments. These activities are being carried out with extra care, especially for cancer patients. Physicians are seeking the help of technology to help diagnose cancer, its dosage, its current status, cancer classification, and appropriate treatment. The machine learning method developed by an artificial intelligence is proposed here in order to effectively assist the doctors in that regard. Its design methods obtain highly complex cancerous inputs and clearly describe its type and dosage. It is also recommending the effects of cancer and appropriate medical procedures to the doctors. This method ensures that a lot of doctors' time is saved. In a saturation point, the proposed model achieved 93.31% of image recognition, 6.69% of image rejection, 94.22% accuracy, 92.42% of precision, 93.94% of recall rate, 92.6% of F1-score, and 2178 ms of computational speed. This shows that the proposed model performs well while compared with the existing methods.

Identifiers

PMID35845582
PMCPMC9283038
OpenAlexW4284894307

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.