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

Journals

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  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