Trustworthy Predictions: An Explainable AI Approach to Breast Cancer Diagnosis

ABSTRACT Breast cancer continues to be one of the leading causes of cancer-related deaths among women worldwide. Early and accurate diagnosis is crucial to improving treatment outcomes and survival rates. This study develops a robust machine learning-based breast cancer classification system utilizing the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Emphasizing the importance of feature selection, the study identifies the most influential tumor characteristics that significantly contribute to malignancy prediction. Multiple classification algorithms—including Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN)—were implemented and rigorously evaluated to determine their predictive performance. After applying feature scaling and selecting optimal features, the SVM model achieved the highest classification accuracy of 96.49%, demonstrating its effectiveness in distinguishing between benign and malignant tumors. To facilitate practical clinical application, the model was deployed via a web-based interface built using Flask, allowing healthcare professionals to input tumor measurements and receive immediate diagnostic predictions. This deployment bridges the gap between complex machine learning models and real-world usability, supporting early detection efforts in clinical settings. The project underscores the potential of combining advanced computational techniques with intuitive interfaces to improve diagnostic workflows. Future work aims to integrate explainable artificial intelligence (XAI) methods to enhance transparency in prediction outcomes and to extend the model’s applicability by incorporating diverse and larger clinical datasets, thereby increasing its robustness and generalizability.