Artificial Neural Networks for Breast Cancer Risk Prediction: Model Architecture and Reported Performance
DOI:
https://doi.org/10.59667/sjoranm.v18i1.16Keywords:
Artificial Neural Networks (ANNs), Predictive analytics, Breast cancer, Risk factors, Personalized preventionAbstract
Introduction: This article examines the use of artificial neural networks (ANNs) to model breast cancer risk from genetic, hormonal, lifestyle and environmental factors.
Methodology: The described workflow includes data preprocessing, an 80/10/10 training–validation–test split, a neural network with three hidden layers of 64, 32 and 16 neurons, and a sigmoid output. The article describes cross-validation, external testing and SHAP-based interpretation of model predictions.
Results and Discussion: The reported performance measures are accuracy 85%, precision 0.82, recall 0.87, F1-score 0.84 and AUC–ROC 0.91. The discussion considers model interpretability, data quality and class imbalance. These measures summarize the reported classification performance; they do not establish improved patient outcomes or superiority to radiologists or alternative models.
Conclusion: ANN-based modelling may support further research on breast cancer risk prediction. The reported results should be interpreted as model performance measures rather than evidence of clinical benefit. Further validation is required before clinical implementation.
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