  Heart failure (HF) poses a significant public health challenge, with a rising
global mortality rate. Early detection and prevention of HF could significantly
reduce its impact. We introduce a novel methodology for predicting HF risk
using 12-lead electrocardiograms (ECGs). We present a novel, lightweight
dual-attention ECG network designed to capture complex ECG features essential
for early HF risk prediction, despite the notable imbalance between low and
high-risk groups. This network incorporates a cross-lead attention module and
twelve lead-specific temporal attention modules, focusing on cross-lead
interactions and each lead's local dynamics. To further alleviate model
overfitting, we leverage a large language model (LLM) with a public ECG-Report
dataset for pretraining on an ECG-report alignment task. The network is then
fine-tuned for HF risk prediction using two specific cohorts from the UK
Biobank study, focusing on patients with hypertension (UKB-HYP) and those who
have had a myocardial infarction (UKB-MI).The results reveal that LLM-informed
pre-training substantially enhances HF risk prediction in these cohorts. The
dual-attention design not only improves interpretability but also predictive
accuracy, outperforming existing competitive methods with C-index scores of
0.6349 for UKB-HYP and 0.5805 for UKB-MI. This demonstrates our method's
potential in advancing HF risk assessment with clinical complex ECG data.
