2 Parallel Heart: Parallel Experiments of a Highly Accurate Dual Independent Neural Network for Predicting Myocardial Infarction in Service of Clinical Practice

OBJECTIVE: To enhance acute myocardial infarction (AMI) prediction accuracy and interpretability by developing a dual-model parallel framework integrating electrocardiography (ECG) signals and blood biochemical markers. METHODS: A convolutional neural network (CNN) extracted ECG features, while a backpropagation (BP) network analyzed 13 biochemical markers (troponin I/T, CK-MB, NT-proBNP, glucose, and the standard chemistry panel). Models were trained and validated on the PTB-XL ECG dataset (n=21,799), the MIMIC-III biochemical database (n=4,760), and a Chinese ECG dataset ($n=503$) for regional calibration. RESULTS: The CNN-based ECG model achieved an AUC of 0.933, with P-R interval and QRS duration as significant predictors. The BP-based biochemical model achieved an AUC of 0.960. The integrated parallel framework achieved a total accuracy of 96.6%, significantly reducing missed diagnoses for non-ST-segment elevation myocardial infarction (NSTEMI) patients with inconspicuous ECG changes. CONCLUSION: This dual-model framework provides an efficient automated screening tool for AMI, bridging ECG and biochemical diagnostics and highlighting the importance of ethnic-specific calibration. SIGNIFICANCE: By recovering NSTEMI cases that ECG-only models systematically miss and by showing that a regional cohort can recalibrate a European-trained classifier, the framework extends automated AMI screening to the presentations and populations where it currently fails most often.