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BACKGROUND: Left ventricular hypertrophy (LVH) is classified as concentric or eccentric based on left ventricle relative wall thickness. Using machine learning (ML) techniques on basic clinical parameters and features from a single-lead electrocardiogram (ECG) we detected LVH in a hypertensive population without cardiovascular disease.METHODS: We enrolled 812 hypertensive subjects with no indications of cardiovascular disease. Based on left ventricular mass index (LVMi) and relative wall thickness (RWT), the subjects were classified into 2 groups; i) normal geometry (NG) and concentric remodeling (CR) were classified as no-LVH, whereas ii) concentric and eccentric hypertrophy were classified as having LVH. We trained a Random Forest to distinguish between the two categories. For comparison we also trained a logistic regression and a convolutional neural network model. We performed feature importance and interaction analysis using SHAP to interpret the model's predictions for feature importance and feature interactions.RESULTS: Our model was able to distinguish no-LVH subjects from the ones with LVH with ROC/AUC 0.82 (95% CI: 0.71-0.91) and average precision 0.61. At threshold 0.3, specificity is 81% and sensitivity is 58%. Age, corrected QT interval, T wave duration, and being female, were the most important features that contributed to the model's predictions.CONCLUSIONS: Utilizing analysis of single-lead ECG combined with clinical data, we demonstrated strong performance in detecting LVH. The identification of key ECG features such as corrected QT interval and T wave morphology underscores the clinical value of single-lead ECG analysis. These findings are particularly important in the era of wearable devices, where accessible, noninvasive screening for cardiac conditions like LVH can be integrated into everyday health monitoring.