Machine learning models enhance detection of arrhythmogenic right ventricular cardiomyopathy.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable cardiac disorder associated with sudden cardiac death, yet its diagnosis remains slow, resource-intensive, and dependent on expert interpretation of multimodal tests. Machine learning approaches may enable earlier and more standardized detection. Here, we sought to identify an optimal machine learning strategy for ARVC detection, and to define its role within the diagnostic pathway. A composite dataset of 688 patients from the Johns Hopkins ARVC Registry was used to train and evaluate eight models based on 31 clinical, electrocardiogram (ECG), imaging, and genetic variables. Following an 80/20 train-test split, models were developed on the train set using five-fold stratified cross-validation and evaluated on the hold-out test cohort. Performance was assessed using area under the curve (AUC), accuracy, sensitivity, specificity, and 95% confidence intervals. Model comparison was performed using the Friedman test with Nemenyi post-hoc analysis. Gradient boosted trees (GBT) achieved the highest overall performance (AUC = 0.943, 95% CI: 0.902-0.99; accuracy = 0.847, 95% CI: 0.786-0.907). Nemenyi testing showed GBT significantly outperformed Decision Tree and TabNet (p < 0.05), while differences vs the remaining five models were not statistically significant. The next-best model (Random Forest) showed a minimal performance gap (ΔAUC = 0.008), whereas low-ranked models showed larger deficits (ΔAUC = 0.040-0.042). An ECG-only version of the GBT model achieved an AUC of 0.884, exceeding the previously reported ECG-deep learning waveform model (AUC = 0.87). GBT performs best among evaluated algorithms and offers clinically interpretable feature relevance consistent with task force criteria for ARVC diagnosis. An ECG-only deployment supports early triage, while the multimodal model functions as confirmatory decision support after advanced testing. These findings support a tiered ML-assisted diagnostic strategy for ARVC and justify prospective external validation in broader clinical settings.