Evidence map›Paper›PMID 33541410›Full record

ArticleBioData mining2021

The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation.

Davide Chicco, Niklas Tötsch, Giuseppe Jurman

Open access · goldAbstract read
In one paragraph

Article in BioData mining, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 243 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
243citing papers in PubMed, 3 pooled it
89.4field-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

243 citing papers in PubMed, 3 syntheses or guidelines pooled it, 859 citations in OpenAlex.

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183 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 3 countries.

Davide Chicco *Krembil Research Institute, Toronto, Ontario, Canada. davidechicco@davidechicco.it.ORCID http://orcid.org/0000-0001-9655-7142
Niklas Tötsch *Universität Duisburg-Essen, Essen, Germany.
Giuseppe JurmanFondazione Bruno Kessler, Trento, Italy.
Fondazione Bruno Kessler · ITKrembil Foundation · CAUniversity of Duisburg-Essen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evaluating binary classifications is a pivotal task in statistics and machine learning, because it can influence decisions in multiple areas, including for example prognosis or therapies of patients in critical conditions. The scientific community has not agreed on a general-purpose statistical indicator for evaluating two-class confusion matrices (having true positives, true negatives, false positives, and false negatives) yet, even if advantages of the Matthews correlation coefficient (MCC) over accuracy and F

Indexed as

Balanced accuracyBinary classificationBookmaker informednessConfusion matrixMachine learningMarkednessMatthews correlation coefficient

Identifiers

PMID33541410
PMCPMC7863449
OpenAlexW3126232929

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.