Breast Cancer Prediction Enhanced by Robotic Automation: Integrating Histological Subtypes, Lymph Node Awareness, Evolutionary Optimized Neuro-Fuzzy Systems, and Dynamic Temporal Graph Modeling

Breast cancer remains one of the largest health concerns in the modern world, characterized by high mortality and complex diagnosis. Since it is erratic and heterogeneous, it requires sophisticated and all-round approaches to precise detection and personalized treatment. The current study proposes an integrated predictive system based on histological subtype classification via convolutional neural networks (CNNs), lymph node metastasis prediction via logistic regression, and uncertainty management via evolutionary optimized neuro-fuzzy systems. Temporal cancer development dynamics are encoded using graph neural networks, and reproducible, optimal analysis is facilitated through robotic automation with minimal human interaction. Experimental outcomes indicate outstanding improvement with overall prediction accuracy of 95.4%, precision of 96.0%, robustness of 93.9%, and inference time of 8. 7 ms. Clinical interpretability also gets enhanced in the new solution with a score of 92.5% and has noisy input data resistance. With the help of high-performance AI methods and automation, the platform offers actionable insights that have the potential to increase diagnostic accuracy and clinical decision-making for breast cancer treatment.