Predictive value of machine learning algorithm of coronary artery calcium score and clinical factors for obstructive coronary artery disease in hypertensive patients

BACKGROUND: The addition of coronary artery calcium score (CACS) to prediction models has been verified to improve performance. Machine learning (ML) algorithms become important medical tools in an era of precision medicine, However, combined utility by CACS and ML algorithms in hypertensive patients to forecast obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CCTA) is rare. METHODS: This retrospective study was composed of 1,273 individuals with hypertension and without a history of CAD, who underwent dual-source computed tomography evaluation. We applied five ML algorithms, coupled with clinical factors, imaging parameters, and CACS to construct predictive models. Moreover, 80% individuals were randomly taken as a training set on which 5-fold cross-validation was done and the remaining 20% were regarded as a validation set. RESULTS: 16.7% (212 out of 1,273) of hypertensive patients had obstructive CAD. Extreme Gradient Boosting (XGBoost) posted the biggest area under the receiver operator characteristic curve (AUC) of 0.83 in five ML algorithms. Continuous net reclassification improvement (NRI) was 0.55 (95% CI (0.39–0.71), p < 0.001), and integrated discrimination improvement (IDI) was 0.04 (95% CI (0.01–0. 07), p = 0.0048) when the XGBoost model was compared with traditional Models. In the subgroup analysis stratified by hypertension levels, XGBoost still had excellent performance. CONCLUSION: The ML model incorporating clinical features and CACS may accurately forecast the presence of obstructive CAD on CCTA among hypertensive patients. XGBoost is superior to other ML algorithms. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-023-02352-8.