Risk Assessment Score and χ2 Automatic Interaction Detection Algorithm for Hypertension Among Africans: Models From the SIREN Study

Document Type



Internal Medicine (East Africa)



This study aimed to develop a risk-scoring model for hypertension among Africans. Methods:

In this study, 4413 stroke-free controls were used to develop the risk-scoring model for hypertension. Logistic regression models were applied to 13 vascular risk factors. We randomly split the data set into training and testing data at a ratio of 80:20. Constant and standardized weights were assigned to factors significantly associated with hypertension in the regression model to develop a probability risk score on a scale of 0% to 100% using a logistic regression model. The model accuracy was assessed to estimate the cutoff score for discriminating between hypertensives.


Mean age was 59.9±13.3, 56.0% were hypertensives, and 7 factors, including diabetes, age in years, waist circumference, body mass index, highest education completed, family history of cardiovascular diseases, and current alcohol use, were associated with hypertension. Cohen κ was maximal at ≥0.28, and a total probability risk score of ≥0.60 was adopted for both statistical weighting for risk quantification of hypertension in both data sets. The probability risk score presented a good performance—receiver operating characteristic: 64% (95% CI, 61.0–68.0), a sensitivity of 55.1%, specificity of 71.5%, positive predicted value of 70.9%, and negative predicted value of 55.8%, in the test data set. Similarly, decision tree had a predictive accuracy of 67.7% (95% CI, 66.1–69.3) for the training set and 64.6% (95% CI, 61.0–68.0) for the testing data set.


The novel risk assessment model discriminated hypertensives with good accuracy and will be helpful in the early identification of community-based Africans vulnerable to hypertension for its primary prevention.

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