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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Prediction of Prehypertenison and Hypertension Based on Anthropometry, Blood Parameters, and Spirometry
Byeong Mun Heo, Keun Ho Ryu
International Journal of Environmental Research and Public Health (2018) Vol. 15, Iss. 11, pp. 2571-2571
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension
Norma Latif Fitriyani, Muhammad Syafrudin, Ganjar Alfian, et al.
IEEE Access (2019) Vol. 7, pp. 144777-144789
Open Access | Times Cited: 148
Machine Learning for Hypertension Prediction: a Systematic Review
Gabriel F. S. Silva, Thales P. Fagundes, Bruno Casalotti Camillo Teixeira, et al.
Current Hypertension Reports (2022) Vol. 24, Iss. 11, pp. 523-533
Closed Access | Times Cited: 75
Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries
Sheikh Mohammed Shariful Islam, Ashis Talukder, Md. Abdul Awal, et al.
Frontiers in Cardiovascular Medicine (2022) Vol. 9
Open Access | Times Cited: 54
Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study
Marenao Tanaka, Yukinori Akiyama, Kazuma Mori, et al.
Clinical and Experimental Hypertension (2025) Vol. 47, Iss. 1
Open Access
A Comparative Analysis of Machine Learning Methods for Class Imbalance in a Smoking Cessation Intervention
Khishigsuren Davagdorj, Jong-Seol Lee, Van-Huy Pham, et al.
Applied Sciences (2020) Vol. 10, Iss. 9, pp. 3307-3307
Open Access | Times Cited: 33
Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction
V Mareeswari, Jayalakshmi Periyasamy, Muthamilselvan Thangavel, et al.
Diagnostics (2023) Vol. 13, Iss. 11, pp. 1942-1942
Open Access | Times Cited: 9
Reconstruction error based deep neural networks for coronary heart disease risk prediction
Tsatsral Amarbayasgalan, Kwang Ho Park, Jong Yun Lee, et al.
PLoS ONE (2019) Vol. 14, Iss. 12, pp. e0225991-e0225991
Open Access | Times Cited: 27
An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records
Xiaolin Diao, Yanni Huo, Zhanzheng Yan, et al.
JMIR Medical Informatics (2020) Vol. 9, Iss. 1, pp. e19739-e19739
Open Access | Times Cited: 18
Intra-person multi-task learning method for chronic-disease prediction
Gihyeon Kim, Heeryung Lim, Yunsoo Kim, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 5
Evaluation of machine learning techniques for hypertension risk prediction based on medical data in Bangladesh
Md. Asadullah, Md. Murad Hossain, Sabrina Rahaman, et al.
Indonesian Journal of Electrical Engineering and Computer Science (2023) Vol. 31, Iss. 3, pp. 1794-1794
Open Access | Times Cited: 5
Hypertension risk prediction models for patients with diabetes based on machine learning approaches
Yuxue Zhao, Jiashu Han, Xinlin Hu, et al.
Multimedia Tools and Applications (2023) Vol. 83, Iss. 20, pp. 59085-59102
Closed Access | Times Cited: 3
A risk scoring system to predict the risk of new‐onset hypertension among patients with type 2 diabetes
Cheng‐Chieh Lin, Chia‐Ing Li, Chiu‐Shong Liu, et al.
Journal of Clinical Hypertension (2021) Vol. 23, Iss. 8, pp. 1570-1580
Open Access | Times Cited: 8
Heart Disease Prediction using Ensemble Learning
Yasmeen Shaikh, V. K. Parvati, Shankar Biradar
(2023), pp. 1-5
Closed Access | Times Cited: 2
Identification of Metabolic Syndrome Based on Anthropometric, Blood and Spirometric Risk Factors Using Machine Learning
Sang Yeob Kim, Gyeong Hee Nam, Byeong Mun Heo
Applied Sciences (2020) Vol. 10, Iss. 21, pp. 7741-7741
Open Access | Times Cited: 5
Local Interpretable Model-Agnostic Explanations of Predictive Models for Hypertension
Khishigsuren Davagdorj, Meijing Li, Keun Ho Ryu
Smart innovation, systems and technologies (2021), pp. 426-433
Closed Access | Times Cited: 5
Modelling an Efficient Clinical Decision Support System for Heart Disease Prediction Using Learning and Optimization Approaches
K. Sridharan
Computer Modeling in Engineering & Sciences (2022) Vol. 131, Iss. 2, pp. 677-694
Open Access | Times Cited: 3
Sex differences in hypertension incidence and risk factors: a population-based cohort study in Southern Iran
Abbas Rezaianzadeh, Masoumeh Ghoddusi Johari‬, Najibullah Baeradeh, et al.
BMC Public Health (2024) Vol. 24, Iss. 1
Open Access
Development of a convenient and effective hypertension risk prediction model and exploration of the relationship between Serum Ferritin and Hypertension Risk: a study based on NHANES 2017—March 2020
Shuang Guo, Jiu-Xin Ge, Shan-Na Liu, et al.
Frontiers in Cardiovascular Medicine (2023) Vol. 10
Open Access | Times Cited: 1
Indeks Massa Tubuh (IMT) Sebagai Prediktor Hipertensi: Perbandingan Standar WHO dan Asia-Pasifik
Indri Mulyasari, Puji Afiatna, Sugeng Maryanto, et al.
Amerta Nutrition (2023) Vol. 7, Iss. 2SP, pp. 247-251
Open Access | Times Cited: 1
Assessment of Anthropometric and Body Composition Risk Factors in Patients with both Hypertension and Stroke in the Korean Population
Sang Yeob Kim, Gyeong Hee Nam
Applied Sciences (2020) Vol. 10, Iss. 9, pp. 3046-3046
Open Access | Times Cited: 3
Determinants of Longitudinal Change of Lung Function in Different Gender in a Large Taiwanese Population Follow-Up Study Categories: Original Investigation
Chia-Heng Chang, Szu‐Chia Chen, Jiun‐Hung Geng, et al.
Journal of Personalized Medicine (2021) Vol. 11, Iss. 10, pp. 1033-1033
Open Access | Times Cited: 3
Aplicação da regressão logística na análise dos dados dos fatores de risco associados à hipertensão arterial
Maria Beatriz Galdino da Silveira, Nyedja Fialho Morais Barbosa, Ana Patrícia Bastos Peixoto, et al.
Research Society and Development (2021) Vol. 10, Iss. 16, pp. e20101622964-e20101622964
Open Access | Times Cited: 2
Cost-Sensitive Neural Network for Prediction of Hypertension Using Class Imbalance Dataset
Khishigsuren Davagdorj, Jong-Seol Lee, Kwang Ho Park, et al.
Smart innovation, systems and technologies (2021), pp. 44-51
Open Access | Times Cited: 2
Development and validation of a model to predict the risk of hypertension using anthropometric indicators in the Chinese population: a retrospective cohort study.
Lixia Wang, Yiwen Wang, Tuo Han, et al.
PubMed (2023) Vol. 15, Iss. 3, pp. 2207-2219
Closed Access
Hypertension Prediction by Using Machine Learning Algorithm Based on Physiological Parameters
Ravinder Kumar, Aman Pankaj Adatia, Gurpreet Singh Wander, et al.
Lecture notes in networks and systems (2023), pp. 495-503
Closed Access
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