Comparison of Feature Extraction Methods for Prognosis Prediction in Hereditary Arrhythmia Disease

Abstract

This study presented multiple prognosis prediction methods for Long QT Syndrome type 2 (LQT2), a hereditary arrhythmic disease. Specifically, we compared the effectiveness of a traditional method using Multiple Sequence Alignment (MSA) with that of a Foundation model (ProtBert) pre-trained on a large dataset without MSA. The results indicated that the method using ProtBert with reconstruction showed the highest prognostic accuracy, suggesting that it is effective in predicting LQT2 prognosis. It is also applicable to the analysis of genetic variants, and this method may be particularly useful for prognosis prediction in situations where annotation costs are high and labeled data sets are scarce.