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. 2022 Dec 29;47(1):1.
doi: 10.1007/s10916-022-01900-5.

Machine Learning in Hypertension Detection: A Study on World Hypertension Day Data

Sara Montagna #  1 , Martino Francesco Pengo #  2   3 , Stefano Ferretti  4 , Claudio Borghi  5 , Claudio Ferri  6 , Guido Grassi  3 , Maria Lorenza Muiesan  7   8 , Gianfranco Parati  2   3
Affiliations

Machine Learning in Hypertension Detection: A Study on World Hypertension Day Data

Sara Montagna et al. J Med Syst. .

Abstract

Many modifiable and non-modifiable risk factors have been associated with hypertension. However, current screening programs are still failing in identifying individuals at higher risk of hypertension. Given the major impact of high blood pressure on cardiovascular events and mortality, there is an urgent need to find new strategies to improve hypertension detection. We aimed to explore whether a machine learning (ML) algorithm can help identifying individuals predictors of hypertension. We analysed the data set generated by the questionnaires administered during the World Hypertension Day from 2015 to 2019. A total of 20206 individuals have been included for analysis. We tested five ML algorithms, exploiting different balancing techniques. Moreover, we computed the performance of the medical protocol currently adopted in the screening programs. Results show that a gain of sensitivity reflects in a loss of specificity, bringing to a scenario where there is not an algorithm and a configuration which properly outperforms against the others. However, Random Forest provides interesting performances (0.818 sensitivity - 0.629 specificity) compared with medical protocols (0.906 sensitivity - 0.230 specificity). Detection of hypertension at a population level still remains challenging and a machine learning approach could help in making screening programs more precise and cost effective, when based on accurate data collection. More studies are needed to identify new features to be acquired and to further improve the performances of ML models.

Keywords: Data analysis; Hypertension; Prevention.

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Conflict of interest statement

The authors have no competing interests associated with this study, or other interests that might be perceived to influence the results and/or discussion reported in this paper.

Figures

Fig. 1
Fig. 1
Preprocessing flowchart
Fig. 2
Fig. 2
ROC curves for the classification algorithms on the original dataset
Fig. 3
Fig. 3
ROC curve for the best performing model, i.e., the Random-Forest in the undersampling experiment
See this image and copyright information in PMC

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