Аңдатпа
For accurate detection of thyroid cancer stages, including I, II, III, IVA, and IVB, proper treatment planning is necessary in a modern, sustainable medical system. Nowadays, researchers suggest the use of machine learning models for detecting complex patterns from laboratory test data. Hence, this work presents a novel, secure, and reliable fusion model that efficiently predicts stages of thyroid cancer. To design a strong ensemble model, it integrates BERT and XGBoost, which exploits both structural and categorical data to identify patterns from clinical data. Along with this, to improve security and transparency, this model was then integrated with a Blockchain platform, where patient records were securely deployed in an immutable platform. Finally, Performance analysis shows that the proposed framework outperforms other competitive classifiers in detecting the stages accurately. Thus, this work demonstrates an effective and secure framework that has the power to identify deep contextual meaning for predicting thyroid cancer stages and utilizes blockchain technology for enhancing privacy and transparency for securing diagnostic information.