BERT and RoBERTa Model Based Approach for Text to Diseases Classification

Since health is the biggest concern in the industry nowadays, this research aims to develop an advanced system that uses transformer-based models, specifically BERT and RoBERTa to detect and classify diseases from medical texts, specifically BERT and RoBERTa While electronic health records, clinical cases and medical volumes are increasing exponentially, more efficient and more accurate classification tools. Where from within us the method leverages the potential of pre-trained Transformer models, fine-tuning them to well- defined medical profiles and for understanding and processing complex medical language in this system modifies this model to identify and classify diseases from clinical notes and other medical texts, thus accurately and efficiently Improvements in there. The system is designed to support healthcare professionals by providing a reliable data-driven tool that streamlines the research process. This reduces their workload and improves patient care through timely and consistent diagnosis. By addressing significant gaps in current textual analysis methods, our work aims to maximize the quality and efficiency of health care delivery. Our approach begins by refining the pre-trained BERT and RoBERTa models on specific medical data, modifying their parameters to better understand the relevant medical terminology and context. We then rigorously examine these models to ensure their reliability and effectiveness in disease classification. The potential benefits are substantial: improved clinical decision support, faster and more accurate diagnosis, and reduced workload for health care providers. This research highlights the flexibility of natural language processing (NLP) integration.