“Advanced Natural Language Processing Techniques for Heart Disease Analysis from Medical Reports”,

Abstract

This work investigates the use of sophisticated pre-trained language models, such as DistilBERT, DistilRoBERTa, and BETO, for heart disease analysis in the healthcare domain, especially for the classification of heart illness using textual data extracted from Colour Doppler Echocardiogram reports. The study used the "finite automata/Beto- heart disease -analysis" tokenizer specifically designed for the Spanish medical language to preprocess patient data for improved model training and assessment. The experimental configuration used state-of-the-art GPUs, fine-tuned hyperparameters, and the Hugging Face Transformers library. DistilRoBERTa demonstrated superior performance compared to DistilBERT and BETO, with an accuracy of 93.38% as opposed to 91.17%. These models exhibited substantial improvements in accuracy, recall, and F1 score when compared to conventional machine learning approaches. The research emphasizes the significance of model design and training methodologies, emphasizing the enhanced contextual comprehension of pre-trained models, resulting in improved accuracy in heart disease categorization. The sustainability plan encompasses the environmental, economic, social, ethical, and community dimensions to guarantee the responsible and enduring utilisation of these models in healthcare. Some important tactics to consider include optimizing resource allocation, implementing cost-efficient solutions, adhering to ethical principles in AI, and fostering cooperation across different disciplines. This

study makes a substantial contribution by conducting a thorough examination of pre- trained language models in heart disease analysis and proposing techniques for their

long-term implementation. The results demonstrate the potential of these models to revolutionize healthcare by improving diagnostic precision and patient care via sophisticated heart disease analysis. Future research should prioritize the optimization of model training, the extension of applications to other medical fields, and the maintenance of ongoing development in accordance with technology improvements and regulatory needs.