A Data-Driven Procedure of Providing a Health Promotion Program for Hypertension Prevention

How can service providers efficiently and effectively implement a health promotion program to prevent hypertension? In this article, the authors propose a two-step procedure based on data analytics using (1) a prediction model to identify people who are at risk for developing hypertension and (2) four methods to create an index that represents the importance of specific intervention programs for eliminating risk factors for individuals. The authors use the national sample cohort database of South Korea to offer a case study of the implementation of the proposed procedure. The constructed prediction model using logistic regression has adequate accuracy, and the proposed index has results similar to those of a doctor. The insight for hypertension prevention program implementation: This two-step procedure, which relies on automatic modeling based on data, can provide informative and personalized results based on individual health records.