ArticlePloS one2023
Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 42 citations in OpenAlex.
- Artificial Intelligence for Chronic Disease Prevention and Management in Sub-Saharan Africa: A World Health Organization Health Systems Building Blocks Perspective.Mayo Clinic proceedings. Digital health · 2026Review
- Multi-horizon machine learning for population-level hypertension risk stratification in the UK Biobank.European heart journal. Digital health · 2026Article
- Harnessing data science to control non-communicable diseases in Africa: a systematic review and gap analysis.Communications medicine · 2026Article
- Machine learning approaches to identify influential factors associated with hypertension and prehypertension among rural adults in Bangladesh.Journal of public health research · 2026Article
- Machine learning approaches in identifying factors associated with hypertension and undiagnosed hypertension in adults in rural areas of Bangladesh.Archives of public health = Archives belges de sante publique · 2026Article
- A Comparison of Machine Learning Algorithms for Predicting Hypertension Incidence Based on Cohort Study.Endocrinology, diabetes & metabolism · 2026Article
- Association of Mobile-Enhanced Remote Patient Monitoring with Blood Pressure Control in Hypertensive Patients with Comorbidities: A Multicenter Pre-Post Evaluation.Diagnostics (Basel, Switzerland) · 2026Article
- Can artificial intelligence bridge critical gaps in hypertension prediction and risk assessment in low- and middle-income countries? A scoping review.BMJ public health · 2026Article
- Leveraging machine learning algorithm to predict minimum dietary diversity among children aged 6-23 months in Ethiopia.PLOS global public health · 2026Article
- Machine-Learning-Based Prediction of Hypertension and Its Risk Factors Among Adults in the Northern Region of Bangladesh.Journal of diabetes research · 2026Article
- Sociodemographic and lifestyle determinants of blood pressure in adult Nigerians.International health · 2025Article
- Screening hypertension using non-laboratory risk factors with machine learning: a retrospective cross-sectional study in Indonesia.BMJ open · 2025Article
- Explainable AI in early autism detection: a literature review of interpretable machine learning approaches.Discover mental health · 2025Review
- Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques.Health science reports · 2025Article
- Two-Year Hypertension Incidence Risk Prediction in Populations in the Desert Regions of Northwest China: Prospective Cohort Study.Journal of medical Internet research · 2025Article
- Factors associated with the presence of anxiety and depression symptoms in rural hypertensive adults in Bangladesh: leveraging extreme gradient booster machine learning algorithm.Frontiers in psychology · 2025Article
- Optimising test intervals for individuals with type 2 diabetes: A machine learning approach.PloS one · 2025Article
- Prediction of primary Hypertension in Primary Health Care Settings in Coastal Karnataka Using Artificial Neural Network.Current hypertension reviews · 2025Article
- Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach.PloS one · 2025Article
- Predicting Job Burnout Among Female Nurses in China With Machine Learning and Shapley Additive Explanations.Journal of nursing management · 2025Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesHypertension (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
methodsThe 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.
resultsThe 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.
conclusionsThe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.