Extracting Knowledge from Machine Learning Models to Diagnose Breast Cancer.

This study explored the application of explainable machine learning models to enhance breast cancer diagnosis using serum biomarkers, contrary to many studies that focus on medical images and demographic data. The primary objective was to develop models that are not only accurate but also provide insights into the factors driving predictions, addressing the need for trustworthy AI in healthcare. Several classification models were evaluated, including OneR, JRIP, the FURIA, J48, the ADTree, and the Random Forest, all of which are known for their explainability. The dataset included a variety of biomarkers, such as electrolytes, metal ions, marker proteins, enzymes, lipid profiles, peptide hormones, steroid hormones, and hormone receptors. The Random Forest model achieved the highest accuracy at 99.401%, followed closely by JRIP, the FURIA, and the ADTree at 98.802%. OneR and J48 achieved 98.204% accuracy. Notably, the models identified oxytocin as a key predictive biomarker, with most models featuring it in their rules. Other significant parameters included GnRH, β-endorphin, vasopressin, IRAP, and APB, as well as factors like iron, cholinesterase, the total protein, progesterone, 5-nucleotidase, and the BMI, which are considered clinically relevant to breast cancer pathogenesis. This study discusses the roles of the identified parameters in cancer development, thus underscoring the potential of explainable machine learning models for enhancing early breast cancer diagnosis by focusing on explainability and the use of serum biomarkers.The combination of both can lead to improved early detection and personalized treatments, emphasizing the potential of these methods in clinical settings. The identified markers also provide additional research and therapeutic targets for breast cancer pathogenesis and a deep understanding of their interactions, advancing personalized approaches to breast cancer management.