Fine Tuning Bert Based Approach for Cardiovascular Disease Diagnosis
Keywords:
Cardio Vascular Disease/Heart disease, Bi-directional Encoder Representations from Transformers (BERT), Fine TuningAbstract
A range of diseases that affect your heart are considered to as coronary disease. The greatest hazard syndromes in the world, cardiovascular diseases (CVD) are regarded as having the greatest mortality rates. The healthcare systems of countries they have become very common and are now overstretching, over a period of time. Cholesterol, family background, higher The arterial pressure is, gender, stress, ageing that are an unhealthy lifestyle are the main danger elements for cardiovascular diseases. Predicated with these considerations, Experts have suggested others preliminary diagnosis techniques. Therefore, correct CVD prediction can both prevent life-threatening conditions and be fatal if incorrect. The majority times, cardiovascular disease is deadly. Medical diagnosis is a challenging task that is usually performed by domain experts. In the Natural language processing (NLP) field, Bidirectional encoder representations from transformers (BERT) and related models have recently achieved significant success. Contextual embeddings are created by pre-training BERT on a larger training corpus, and these embeddings can be used to enhance the efficiency of CVD data sets when implemented to small datasets with fine-tuning. Hence in this approach, Fine tuning BERT Based Approach for Cardio Vascular Disease diagnosis is presented. This approach will provide effective and accurate diagnosis to the CVD patients. This method's performance is evaluated in related to accuracy, precision, and recall (True Positive Rate).
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