Risk Assessment Score and Chi-Square Automatic Interaction Detection Algorithm for Hypertension among Africans: Models from the SIREN Study

Osahon J. Asowata, Akinkunmi Paul Okekunle, Onoja M. Akpa, Adekunle Gregory Fakunle, Joshua O. Akinyemi, Morenikeji Adeyoyin Komolafe, Fred Stephen Sarfo, Albert K. Akpalu, Reginald Obiako, Kolawole W. Wahab, Godwin O. Osaigbovo(osawaru), Lukman F. Owolabi, Carolyn M. Jenkins, Benedict Nii Laryea Calys-Tagoe, Oyedunni Sola Arulogun, Godwin I. Ogbole, Okechukwu Samuel Ogah, Appiah T. Lambert, Philip Oluleke Ibinaiye, Philip B. AdebayoArti Singh, Sunday Adebori Adeniyi, Yaw B. Mensah, Ruth Y. Laryea, Olayemi Balogun, Innocent Ijezie Chukwuonye, Rufus O. Akinyemi, Bruce Ovbiagele, Mayowa Ojo Owolabi

Research output: Contribution to journalArticlepeer-review


BACKGROUND: 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 risk factors. We randomly split the dataset 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 1 using a logistic regression model. The model accuracy was assessed to estimate the cutoff score for discriminating hypertensives. RESULTS: Mean age was 59.9±13.3 years, 56.0% were hypertensives, and 8 factors, including diabetes, age ≥65 years, higher waist circumference, (BMI) ≥30 kg/m2, lack of formal education, living in urban residence, family history of cardiovascular diseases, and dyslipidemia 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 datasets. 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 dataset. 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 dataset. CONCLUSIONS: The novel risk-scoring 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.

Original languageEnglish
Pages (from-to)2581-2590
Number of pages10
Issue number12
Publication statusPublished - 1 Dec 2023


  • blood pressure
  • body mass index
  • hypertension
  • machine learning
  • risk assessment


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