Tiongco Raphael Enrique, Cruz Bradley Dela, Dizon Lorenz Angelo, Elayda Charles Henry, Dionisio Nicolle, Antalan Jessa, Intal Zsarina, Abenoja Catherine Alexandria, Bautista Hubert Sebastian, Tungol Samcher Chantal, Dayrit Sofia Alexis, Mañalac Arch Raphael, Santillan Jennifer, Navarro Annalyn
Graduate School, Angeles University Foundation, 2009, Angeles City, Philippines.
College of Allied Medical Professions, Angeles University Foundation, 2009, Angeles City, Philippines.
J Cancer Educ. 2025 Aug 16. doi: 10.1007/s13187-025-02690-3.
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.
早期发现乳腺癌至关重要，尤其是在资源匮乏的地区。诸如人工神经网络（ANN）等新兴技术为预测健康行为意图提供了创新方法。因此，本研究使用人工神经网络-多层感知器（ANN-MLP）模型来确定女大学生进行乳房自我检查（BSE）意图的预测因素。经伦理批准，对一所私立高等教育机构的382名女大学生进行了一项分析性横断面研究。使用经过验证的研究问卷收集了社会人口学因素、知识、实践、感知障碍和健康信念等方面的数据。进行了Pearson相关性分析、卡方检验和ANN-MLP建模，以确定BSE意图和实际乳房筛查行为的重要预测因素。知识充足、频繁进行BSE实践、感知易感性和自我效能感是进行BSE意图的重要预测因素。ANN-MLP模型在预测BSE意图方面的测试准确率达到74.1%，曲线下面积（AUC）为0.698。对于实际乳房筛查行为，重要预测因素包括学位课程、知识、实践、自我获益和自我效能感。相应的ANN-MLP模型在预测实际乳房筛查行为时测试准确率达到88.7%，AUC为0.779。认知和行为因素的相互作用显著影响BSE意图。使用ANN-MLP建模可以有效地确定BSE意图的关键预测因素，为强有力的、数据驱动的和有针对性的健康促进干预提供机会。这些发现通过识别BSE意图的认知和行为预测因素，可能为患者、公众和专业癌症教育提供参考，使卫生专业人员能够设计量身定制的教育和培训项目，以增强早期筛查行为并倡导乳腺癌预防。
