Structured-to-text ClinicalBERT embeddings with random Forest for heart disease prediction: a proof-of-concept study on the UCI Statlog dataset.

The study employs a publicly available UCI Statlog/Kaggle heart disease dataset containing 270 complete patient records.

Structured cardiovascular attributes, including age, sex, chest pain type, blood pressure, cholesterol level, electrocardiogram results, and heart rate measurements, are converted into clinically meaningful textual descriptions.

Comparative analysis with conventional machine learning models indicates that contextual embeddings generated by ClinicalBERT provide improved feature representation for cardiovascular risk prediction.

The findings demonstrate the feasibility of adapting ClinicalBERT to structured cardiovascular data through contextual text generation.