Machine learning based prediction of risk of hypertension among people of Tamil Nadu

Abstract

Hypertension is the one of the leading causes of cardiovascular morbidity and mortality. Early detection of individuals at elevated risk is critical, yet conventional prediction models fail to capture nonlinear and high-dimensional interactions among epidemiological and behavioral determinants. Machine learning (ML) offers new opportunities for accurate, population-level risk stratification. We conducted a secondary analysis of the Tamil Nadu STEPS survey 2020, applying supervised ML algorithms—including boosting, k-nearest neighbours, decision tree, random forest, and support vector machine—to predict hypertension risk. Predictors included demographic, anthropometric, lifestyle, and behavioral variables. Models were implemented in JASP, and their performance was evaluated using accuracy, area under the receiver operating characteristic curve (AUC), precision, recall, and F1 score. Among the algorithms tested, the random forest classifier demonstrated the most balanced performance (accuracy 65.5%, AUC 0.708, precision 64.2%, recall 65.5%, F1 score 64.7%). Feature importance analysis identified age as the strongest predictor, followed by waist circumference, while diet and physical activity contributed minimally. The confusion matrix confirmed the model’s balanced sensitivity and specificity, reducing both false negatives and false positives. This study highlights the potential of machine learning, particularly random forest models, for hypertension risk prediction in Indian populations. By leveraging routinely collected survey data, ML can enable scalable, non-invasive screening and inform targeted public health interventions. Integration of richer clinical and genetic features and ensemble methods may further improve predictive accuracy.