Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Aug 24;18(8):e0289613.
doi: 10.1371/journal.pone.0289613. eCollection 2023.

Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia

Md Merajul Islam  1   2 , Md Jahangir Alam  2   3 , Md Maniruzzaman  4 , N A M Faisal Ahmed  5 , Md Sujan Ali  6 , Md Jahanur Rahman  2 , Dulal Chandra Roy  2
Affiliations

Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia

Md Merajul Islam et al. PLoS One. .

Abstract

Background and objectives: Hypertension (HTN), a major global health concern, is a leading cause of cardiovascular disease, premature death and disability, worldwide. It is important to develop an automated system to diagnose HTN at an early stage. Therefore, this study devised a machine learning (ML) system for predicting patients with the risk of developing HTN in Ethiopia.

Materials and methods: The HTN data was taken from Ethiopia, which included 612 respondents with 27 factors. We employed Boruta-based feature selection method to identify the important risk factors of HTN. The four well-known models [logistics regression, artificial neural network, random forest, and extreme gradient boosting (XGB)] were developed to predict HTN patients on the training set using the selected risk factors. The performances of the models were evaluated by accuracy, precision, recall, F1-score, and area under the curve (AUC) on the testing set. Additionally, the SHapley Additive exPlanations (SHAP) method is one of the explainable artificial intelligences (XAI) methods, was used to investigate the associated predictive risk factors of HTN.

Results: The overall prevalence of HTN patients is 21.2%. This study showed that XGB-based model was the most appropriate model for predicting patients with the risk of HTN and achieved the accuracy of 88.81%, precision of 89.62%, recall of 97.04%, F1-score of 93.18%, and AUC of 0. 894. The XBG with SHAP analysis reveal that age, weight, fat, income, body mass index, diabetes mulitas, salt, history of HTN, drinking, and smoking were the associated risk factors of developing HTN.

Conclusions: The proposed framework provides an effective tool for accurately predicting individuals in Ethiopia who are at risk for developing HTN at an early stage and may help with early prevention and individualized treatment.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Workflow of the proposed ML-based methodology for predicting risk of HTN.
Fig 2
Fig 2. Risk factors selection using Boruta based feature selection method.
Fig 3
Fig 3
(a) ROC curves and (b) Precision vs. recall curves of four predictive models.
Fig 4
Fig 4. Importance of risk factors based on SHAP values.
(A) Mean absolute SHAP values, to explain global risk factor importance, (B) Local explanation summary, to reveal the direction of the relationship between a risk factor and game outcome.
See this image and copyright information in PMC

References

    1. Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nature Reviews Nephrology. 2020;16(4):223–37. doi: 10.1038/s41581-019-0244-2 - DOI - PMC - PubMed
    1. GBD 2017 Risk Factor Collaborators. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018; 392:1923–94. doi: 10.1016/S0140-6736(18)32225-6 - DOI - PMC - PubMed
    1. GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1736–88. doi: 10.1016/S0140-6736(18)32203-7 - DOI - PMC - PubMed
    1. Gupta R, Xavier D. Hypertension: the most important non communicable disease risk factor in India. Indian heart journal. 2018;70(4):565–72. doi: 10.1016/j.ihj.2018.02.003 - DOI - PMC - PubMed
    1. Fuchs FD, Whelton PK. High blood pressure and cardiovascular disease. Hypertension. 2020;75(2):285–92. doi: 10.1161/HYPERTENSIONAHA.119.14240 - DOI - PMC - PubMed

LinkOut - more resources