Breast Cancer Risk Prediction: A Machine Learning Study Using Network Analysis
Saiqing Guan
Abstract
New case breast cancer has steadily increased since 2012 and contributes to highest treatment costs among cancers. While current literature widely applies Machine learning (ML) and Artificial Intelligence (AI) in breast cancer prediction using patient features. However, latent relationships between diseases and patients remain unexplored. This study utilizes National Health Interview Survey (NHIS) 2023 and focused on chronic diseases participants. 738 breast cancer patients and 733 non-breast cancer patients are randomly selected. Patient-centric network attributes and patient features are both included into seven machine-learning risk prediction models. XGBoost model outperform among others having an Area Under Curve (AUC) of 94%. Notably, eigenvalue centrality and obesity are the most important features. This novel approach represents a significant improvement over current literature and provides promising applications in disease prediction.