Evidence map›Paper›PMID 41277986›Full record

ArticleMethodsX2025

Data preparation method for machine learning-based breast cancer risk prediction: A Cuban case study.

Jose Manuel Valencia-Moreno, Everardo Gutierrez-Lopez, Jose Angel Gonzalez-Fraga, Rodolfo Alan Martinez Rodriguez, Olivia Denisse Victoria Mejia, Alma Alejandra Soberano Serrano

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Jose Manuel Valencia-MorenoUniversidad Autónoma de Baja California (Autonomous University of Baja California), Mexico.
Everardo Gutierrez-LopezUniversidad Autónoma de Baja California (Autonomous University of Baja California), Mexico.
Jose Angel Gonzalez-FragaUniversidad Autónoma de Baja California (Autonomous University of Baja California), Mexico.
Rodolfo Alan Martinez RodriguezUniversidad Autónoma de Baja California (Autonomous University of Baja California), Mexico.
Olivia Denisse Victoria MejiaUniversidad Autónoma de Baja California (Autonomous University of Baja California), Mexico.
Alma Alejandra Soberano SerranoUniversidad Autónoma de Baja California (Autonomous University of Baja California), Mexico.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article presents a dataset of breast cancer risk factors collected from 1697 Cuban women between 2001 and 2018, as a tool to design and support the development and validation of predictive models in public health for breast cancer risk. A reproducible methodology for quality control and variable enrichment was implemented to ensure data integrity and compatibility with machine learning techniques. • Reproducible preprocessing methodology to ensure data quality and traceability. • Open breast cancer risk factor dataset for epidemiological studies and risk assessment using machine learning. • Consistent prediction model performance across multiple metrics after data preprocessing.

Indexed as

Breast cancerCubaData preparationMachine learningPublic healthRisk factors

Identifiers

PMID41277986
PMCPMC12637264

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.