Breast cancer remains one of the leading causes of cancer-related mortality worldwide, emphasizing the need for timely and accurate detection. This research proposes a machine learning-based framework to classify samples as cancerous or non-cancerous by leveraging high-dimensional genomic information combined with clinical data. The core model used is an XGBoost classifier, embedded within a user-friendly web interface, and benchmarked against other models such as SVM, KNN, Decision Tree, Random Forest, and a one- dimensional Convolutional Neural Network (1D CNN). These models were trained and evaluated using the METABRIC dataset. Among them, the 1D CNN achieved the highest performance, with 72% accuracy and a ROC-AUC of 0.71, while XGBoost followed closely with 68% accuracy and an AUC of 0.57. The overall system is built using the Flask framework, allows healthcare professionals to upload gene expression data and obtain instant predictions along with explanations of key contributing features. This study delivers a practical and accessible solution for breast cancer prediction, combining high reliability with clinical interpretability. Keywords: Breast Cancer, Genomic Data, Machine Learning, XGBoost, Convolutional Neural Network, Precision Medicine, Interpretability
S et al. (Thu,) studied this question.