Three New Transformer-based Scientific Validated Paradigms for Classification of Hypertrophic Cardiomyopathy and Acute Myocardial Infarction Patients Using Transcriptomic Gene Data on GPU cluster.

BACKGROUND AND MOTIVATION: Classification of transcriptomic gene data is essential for Cardiovascular disease (CVD) risk, particularly in Hypertrophic Cardiomyopathy (HCM) and Acute Myocardial Infarction (AMI) patients. Existing approaches suffer from limited feature representation and weak biological context modeling. To address these gaps, we propose AtheroEdge™ 5.0, which incorporates three novel Transformers (Xmers): Neuro-Topology (NT), Self-Supervised Contrastive Learning with Alignment and Random Feature Masking (SCARF), and Temporal Diffusion Gene (TDG).

METHOD: Twelve Artificial Intelligence models: three novel Xmers (Models A), three Legacy Xmers (Models B), three Deep Learning (Models C), and three machine learning models (Models D) were designed. Feature engineering included Differential Expression Analysis (DEA) for gene selection and normalization of two different cardiac datasets: HCM and AMI. (iii) Performance was evaluated using K10 cross-validation. The AtheroEdge™ 5.0 was scientifically validated using (a) unseen datasets, (b) K-effect, (c) Generalization-effect, and (d) Local Interpretable Model-agnostic Explanations (LIME)-based Models. Software verification was conducted using Coronary Artery Disease data. Reliability and stability tests were conducted. We hypothesized that: (a) Models A outperform Models B to D, (b) unseen data performance is comparable to seen data for both HCM and AMI datasets, and (c) TDG-Xmer outperforms NT Xmer and SCARF-Xmer.

RESULTS

Model A achieved a mean accuracy superior to Models B, C, and D by 4.01%, 10%, and 23.95%, respectively. The Mean Area-under-the-curve of Models A, B, C, and D were 0.96, 0.95, 0.91, and 0.80, respectively. Performance decline on unseen cohorts remained below 10%, meeting regulatory criteria. 87% of high-risk genes were consistently identified by all three novel Xmers and by DEA. TDG-Xmer outperformed NT-Xmer and SCARF-Xmer by 0.5% and 5.26%, respectively.

CONCLUSIONS

The proposed Xmers provide a robust and scientifically validated framework for accurate CVD risk stratification.