This paper is in the following e-collection/theme issue:

Artificial Intelligence (1409) Machine Learning (1491) Health Services in Resource-Poor Settings and LMICs (423) Diabetes Health Services and System Innovations (119) Diabetes Prevention (86) Hypertension Prevention and Treatment (228)

Published on in Vol 23, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20123, first published .
Risk Stratification for Early Detection of Diabetes and Hypertension in Resource-Limited Settings: Machine Learning Analysis

Risk Stratification for Early Detection of Diabetes and Hypertension in Resource-Limited Settings: Machine Learning Analysis

Risk Stratification for Early Detection of Diabetes and Hypertension in Resource-Limited Settings: Machine Learning Analysis

Authors of this article:

Justin J Boutilier1 Author Orcid Image ;   Timothy C Y Chan2 Author Orcid Image ;   Manish Ranjan3 Author Orcid Image ;   Sarang Deo4 Author Orcid Image
Article Authors Cited by (23) Tweetations (7) Metrics

Journals

  1. Patrício A, Costa R, Henriques R. Predictability of COVID-19 Hospitalizations, Intensive Care Unit Admissions, and Respiratory Assistance in Portugal: Longitudinal Cohort Study. Journal of Medical Internet Research 2021;23(4):e26075 View
  2. Chai S, Goh K, Cheah W, Chang Y, Ng G. Hypertension Prediction in Adolescents Using Anthropometric Measurements: Do Machine Learning Models Perform Equally Well?. Applied Sciences 2022;12(3):1600 View
  3. Liu X, Zhang W, Zhang Q, Chen L, Zeng T, Zhang J, Min J, Tian S, Zhang H, Huang H, Wang P, Hu X, Chen L. Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study. Frontiers in Endocrinology 2022;13 View
  4. Islam S, Talukder A, Awal M, Siddiqui M, Ahamad M, Ahammed B, Rawal L, Alizadehsani R, Abawajy J, Laranjo L, Chow C, Maddison R. Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries. Frontiers in Cardiovascular Medicine 2022;9 View
  5. Tuppad A, Patil S. Machine learning for diabetes clinical decision support: a review. Advances in Computational Intelligence 2022;2(2) View
  6. Jónasson J, Ramdas K, Sungu A. Social impact operations at the global base of the pyramid. Production and Operations Management 2022;31(12):4364 View
  7. Silva G, Fagundes T, Teixeira B, Chiavegatto Filho A. Machine Learning for Hypertension Prediction: a Systematic Review. Current Hypertension Reports 2022;24(11):523 View
  8. Fregoso-Aparicio L, Noguez J, Montesinos L, García-García J. Machine learning and deep learning predictive models for type 2 diabetes: a systematic review. Diabetology & Metabolic Syndrome 2021;13(1) View
  9. Yang C, Liu Q, Guo H, Zhang M, Zhang L, Zhang G, Zeng J, Huang Z, Meng Q, Cui Y. Usefulness of Machine Learning for Identification of Referable Diabetic Retinopathy in a Large-Scale Population-Based Study. Frontiers in Medicine 2021;8 View
  10. du Toit C, Tran T, Deo N, Aryal S, Lip S, Sykes R, Manandhar I, Sionakidis A, Stevenson L, Pattnaik H, Alsanosi S, Kassi M, Le N, Rostron M, Nichol S, Aman A, Nawaz F, Mehta D, Tummala R, McCallum L, Reddy S, Visweswaran S, Kashyap R, Joe B, Padmanabhan S. Survey and Evaluation of Hypertension Machine Learning Research. Journal of the American Heart Association 2023;12(9) View
  11. Jiao W, Zhang X, D’Souza F. The Economic Value and Clinical Impact of Artificial Intelligence in Healthcare: A Scoping Literature Review. IEEE Access 2023;11:123445 View
  12. Chong M, Hickie I, Cross S, McKenna S, Varidel M, Capon W, Davenport T, LaMonica H, Sawrikar V, Guastella A, Naismith S, Scott E, Iorfino F. Digital Application of Clinical Staging to Support Stratification in Youth Mental Health Services: Validity and Reliability Study. JMIR Formative Research 2023;7:e45161 View
  13. Tuppad A, Devi Patil S. An efficient classification framework for Type 2 Diabetes incorporating feature interactions. Expert Systems with Applications 2024;239:122138 View
  14. El-Sherbini A, Hassan Virk H, Wang Z, Glicksberg B, Krittanawong C. Machine-Learning-Based Prediction Modelling in Primary Care: State-of-the-Art Review. AI 2023;4(2):437 View
  15. Guo S, Ge J, Liu S, Zhou J, Li C, Chen H, Chen L, Shen Y, Zhou Q. 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. Frontiers in Cardiovascular Medicine 2023;10 View
  16. Li L, Cheng Y, Ji W, Liu M, Hu Z, Yang Y, Wang Y, Zhou Y. Machine learning for predicting diabetes risk in western China adults. Diabetology & Metabolic Syndrome 2023;15(1) View
  17. Huang A, Huang S. Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension. The Journal of Clinical Hypertension 2023;25(12):1135 View
  18. Al-Zubayer M, Alam K, Shanto H, Maniruzzaman M, Majumder U, Ahammed B. Machine learning models for prediction of double and triple burdens of non-communicable diseases in Bangladesh. Journal of Biosocial Science 2024;56(3):426 View
  19. Nuthakki P, Kumar T. Machine learning-based early detection of diabetes risk factors for improved health management. Multimedia Tools and Applications 2024 View
  20. Abbas S, Avelino Sampedro G, Krichen M, Alamro M, Mihoub A, Kulhanek R. Effective Hypertension Detection Using Predictive Feature Engineering and Deep Learning. IEEE Access 2024;12:89055 View
  21. Addo G, Yeboah B, Obuobi M, Doh-Nani R, Mohammed S, Amakye D. Prediction of Diabetes in Middle-Aged Adults: A Machine Learning Approach. Journal of Diabetology 2024;15(4):401 View
  22. Liu J, Huang W, Hu J, Hong N, Rhee Y, Li Q, Chen C, Chueh J, Lin Y, Wu V. Validating Machine Learning Models Against the Saline Test Gold Standard for Primary Aldosteronism Diagnosis. JACC: Asia 2024;4(12):972 View
  23. Bora G, Kumar R, Joseph A. Early identification of potentially low performing community health workers using an ensemble classification model. International Journal of Productivity and Performance Management 2024 View