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, Sofia Alexis Dayrit, Arch Raphael Mañalac, Jennifer Santillan, Annalyn Navarro
{"title":"当健康行为遇上科技:基于人工神经网路的菲律宾女大学生乳房自检意愿预测。","authors":"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, Sofia Alexis Dayrit, Arch Raphael Mañalac, Jennifer Santillan, Annalyn Navarro","doi":"10.1007/s13187-025-02690-3","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50246,"journal":{"name":"Journal of Cancer Education","volume":" ","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"When Health Behavior Meets Technology: Artificial Neural Network-Based Prediction of Breast Self-Examination Intentions Among Filipino Female College Students.\",\"authors\":\"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, Sofia Alexis Dayrit, Arch Raphael Mañalac, Jennifer Santillan, Annalyn Navarro\",\"doi\":\"10.1007/s13187-025-02690-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Early detection of breast cancer is crucial, particularly in low-resource settings. 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When Health Behavior Meets Technology: Artificial Neural Network-Based Prediction of Breast Self-Examination Intentions Among Filipino Female College Students.
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.
期刊介绍:
The Journal of Cancer Education, the official journal of the American Association for Cancer Education (AACE) and the European Association for Cancer Education (EACE), is an international, quarterly journal dedicated to the publication of original contributions dealing with the varied aspects of cancer education for physicians, dentists, nurses, students, social workers and other allied health professionals, patients, the general public, and anyone interested in effective education about cancer related issues.
Articles featured include reports of original results of educational research, as well as discussions of current problems and techniques in cancer education. Manuscripts are welcome on such subjects as educational methods, instruments, and program evaluation. Suitable topics include teaching of basic science aspects of cancer; the assessment of attitudes toward cancer patient management; the teaching of diagnostic skills relevant to cancer; the evaluation of undergraduate, postgraduate, or continuing education programs; and articles about all aspects of cancer education from prevention to palliative care.
We encourage contributions to a special column called Reflections; these articles should relate to the human aspects of dealing with cancer, cancer patients, and their families and finding meaning and support in these efforts.
Letters to the Editor (600 words or less) dealing with published articles or matters of current interest are also invited.
Also featured are commentary; book and media reviews; and announcements of educational programs, fellowships, and grants.
Articles should be limited to no more than ten double-spaced typed pages, and there should be no more than three tables or figures and 25 references. We also encourage brief reports of five typewritten pages or less, with no more than one figure or table and 15 references.