Evidence map›Paper›PMID 40817992›Full record

ArticleJournal of cancer education : the official journal of the American Association for Cancer Education2026

When Health Behavior Meets Technology: Artificial Neural Network-Based Prediction of Breast Self-Examination Intentions Among Filipino Female College Students.

Raphael Enrique Tiongco, Bradley Dela Cruz, Lorenz Angelo Dizon, Charles Henry Elayda, Nicolle Dionisio, Jessa Antalan, Zsarina Intal, Catherine Alexandria Abenoja, Hubert Sebastian Bautista, Samcher Chantal Tungol and 4 more

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Article in Journal of cancer education : the official journal of the American Association for Cancer Education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

14 authors.

Raphael Enrique TiongcoGraduate School, Angeles University Foundation, 2009, Angeles City, Philippines. tiongco.raphael@auf.edu.ph.ORCID 0000-0002-7793-6558
Bradley Dela CruzCollege of Allied Medical Professions, Angeles University Foundation, 2009, Angeles City, Philippines.
Lorenz Angelo DizonCollege of Allied Medical Professions, Angeles University Foundation, 2009, Angeles City, Philippines.
Charles Henry ElaydaCollege of Allied Medical Professions, Angeles University Foundation, 2009, Angeles City, Philippines.
Nicolle DionisioCollege of Allied Medical Professions, Angeles University Foundation, 2009, Angeles City, Philippines.
Jessa AntalanCollege of Allied Medical Professions, Angeles University Foundation, 2009, Angeles City, Philippines.
Zsarina IntalCollege of Allied Medical Professions, Angeles University Foundation, 2009, Angeles City, Philippines.
Catherine Alexandria AbenojaCollege of Allied Medical Professions, Angeles University Foundation, 2009, Angeles City, Philippines.
Hubert Sebastian BautistaCollege of Allied Medical Professions, Angeles University Foundation, 2009, Angeles City, Philippines.
Samcher Chantal TungolCollege of Allied Medical Professions, Angeles University Foundation, 2009, Angeles City, Philippines.
Sofia Alexis DayritCollege of Allied Medical Professions, Angeles University Foundation, 2009, Angeles City, Philippines.
Arch Raphael MañalacCollege of Allied Medical Professions, Angeles University Foundation, 2009, Angeles City, Philippines.
Jennifer SantillanGraduate School, Angeles University Foundation, 2009, Angeles City, Philippines.
Annalyn NavarroGraduate School, Angeles University Foundation, 2009, Angeles City, Philippines.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early detection of breast cancer is crucial, particularly in low-resource settings. Emerging technologies like artificial neural networks (ANNs) offer innovative approaches to predict health behavior intentions. Hence, this study used an artificial neural network-multilayer perceptron (ANN-MLP) model to determine the predictors of breast self-examination (BSE) intention among female college students. With ethical approval, an analytical cross-sectional study was conducted among 382 female college students from a private higher education institution. Data on sociodemographic factors, knowledge, practices, perceived barriers, and health beliefs were collected using validated research questionnaires. Pearson's correlation, chi-square, and ANN-MLP modeling were performed to identify significant predictors of BSE intention and actual breast screening behavior. Knowledge adequacy, frequent BSE practice, perceived susceptibility, and self-efficacy were significant predictors of intention to perform BSE. The ANN-MLP model achieved a testing accuracy of 74.1% and an AUC of 0.698 in predicting BSE intention. For actual breast screening behavior, significant predictors included degree program, knowledge, practice, self-benefit, and self-efficacy. The corresponding ANN-MLP model achieved 88.7% testing accuracy with an AUC of 0.779. The interplay of cognitive and behavioral factors significantly influences BSE intentions. The use of ANN-MLP modeling can effectively determine key predictors of BSE intentions, offering opportunities for robust, data-driven, and targeted health promotion interventions. The findings may inform patient, public, and professional cancer education by identifying cognitive and behavioral predictors of BSE intention, enabling health professionals to design tailored education and training programs that enhance early screening behaviors and advocacy for breast cancer prevention.

Indexed as

Breast NeoplasmsBreast Self-ExaminationEarly Detection of CancerHealth BehaviorHealth Knowledge, Attitudes, PracticeIntentionNeural Networks, ComputerStudentsAdolescentAdultCross-Sectional StudiesFemaleHumansMultilayer PerceptronsPhilippinesPrediction AlgorithmsArtificial neural networksBreast cancerBreast self-examinationMultilayer perceptron

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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.