AI-Enhanced Symptom Checker Using BioBERT for Disease Prediction

Abstract-Precise disease diagnosis using symptoms is of paramount importance in efficient healthcare but is frequently incomplete in conventional symptom-checking frameworks that depend upon rule-based techniques or sparse data. Pre-trained in biomedical text for transfer learning tasks, the NLP model literature, for predicting diseases through patient-reported symptoms. Through Bio BERT fine-tuning on open-source symptom-disease datasets, the system accurately maps symptoms to potential diseases, overcoming limitations like symptom variability and overlapping disease presentations. The proposed approach is compared with Naive Bayes (NB), as well as other conventional machine learning models, This includes Bayes, Random Forest, and Support Vector Machines(SVM).Experimental outcomes illustrate that the fine-tuned Bio BERT model has an accuracy rate of 89%, surpassing conventional methods by far. The system is also equipped with capabilities to improve and learn over time by incorporating user feedback to enhance its predictions. This study identifies the possibility of AI-driven symptom checkers to transform healthcare by offering real-time, accurate, and individualized disease prediction, alleviating the pressure on healthcare systems, and enhancing patient outcomes.