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

Machine Learning (1501) Data Science (278) Big Data (245) Machine Learning (1501) Coronary Heart Disease (81)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17257, first published .
Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation

Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation

Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation

Authors of this article:

Zhenzhen Du1, 2 Author Orcid Image ;   Yujie Yang1, 3 Author Orcid Image ;   Jing Zheng4 Author Orcid Image ;   Qi Li1 Author Orcid Image ;   Denan Lin4 Author Orcid Image ;   Ye Li1 Author Orcid Image ;   Jianping Fan1 Author Orcid Image ;   Wen Cheng2 Author Orcid Image ;   Xie-Hui Chen5 Author Orcid Image ;   Yunpeng Cai1 Author Orcid Image
Article Authors Cited by (51) Tweetations Metrics

Journals

  1. Zhang Y, Zhang Q, Lv J, Zhang D, Kaddouri M, Al Dulaimi S. Risk factors for diagnosis of Escherichia coli infection after flexible ureteroscope holmium laser lithotripsy by imaging information technology under Nomogram mathematical model. Results in Physics 2021;25:104330 View
  2. Lee J, Kiiskinen T, Mars N, Jukarainen S, Ingelsson E, Neale B, Ripatti S, Natarajan P, Ganna A. Clinical Conditions and Their Impact on Utility of Genetic Scores for Prediction of Acute Coronary Syndrome. Circulation: Genomic and Precision Medicine 2021;14(4) View
  3. Alanazi E, Abdou A, Luo J. Predicting Risk of Stroke From Lab Tests Using Machine Learning Algorithms: Development and Evaluation of Prediction Models. JMIR Formative Research 2021;5(12):e23440 View
  4. Alsaffar M, Alshammari A, Alshammari G, Aljaloud S, Almurayziq T, Abdoon F, Abebaw S, Algalil F. Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing. Applied Bionics and Biomechanics 2021;2021:1 View
  5. Drake C, Lewinski A, Rader A, Schexnayder J, Bosworth H, Goldstein K, Gierisch J, White-Clark C, McCant F, Zullig L. Addressing Hypertension Outcomes Using Telehealth and Population Health Managers: Adaptations and Implementation Considerations. Current Hypertension Reports 2022;24(8):267 View
  6. Moon J, Posada-Quintero H, Chon K. A literature embedding model for cardiovascular disease prediction using risk factors, symptoms, and genotype information. Expert Systems with Applications 2023;213:118930 View
  7. Sato H, Kimura Y, Ohba M, Ara Y, Wakabayashi S, Watanabe H. Prediction of Prednisolone Dose Correction Using Machine Learning. Journal of Healthcare Informatics Research 2023;7(1):84 View
  8. Brites I, da Silva L, Barbosa J, Rigo S, Correia S, Leithardt V. Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review. Informatics 2021;8(4):73 View
  9. Anetta K, Horak A, Wojakowski W, Wita K, Jadczyk T. Deep Learning Analysis of Polish Electronic Health Records for Diagnosis Prediction in Patients with Cardiovascular Diseases. Journal of Personalized Medicine 2022;12(6):869 View
  10. Barbieri S, Mehta S, Wu B, Bharat C, Poppe K, Jorm L, Jackson R. Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach. International Journal of Epidemiology 2022;51(3):931 View
  11. Tan K, Seng J, Kwan Y, Chen Y, Zainudin S, Loh D, Liu N, Low L. Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review. Journal of Diabetes Science and Technology 2023;17(2):474 View
  12. Twarish Alhamazani K, Alshudukhi J, Aljaloud S, Abebaw S, Koundal D. [Retracted] Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives. Computational Intelligence and Neuroscience 2021;2021(1) View
  13. Lin C, Lee Y, Wu F, Lin S, Hsu C, Lee C, Tsai D, Fang W. The Application of Projection Word Embeddings on Medical Records Scoring System. Healthcare 2021;9(10):1298 View
  14. Yang H, Chen Z, Yang H, Tian M. Predicting Coronary Heart Disease Using an Improved LightGBM Model: Performance Analysis and Comparison. IEEE Access 2023;11:23366 View
  15. 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
  16. Bai C, Al-Ani M, Amini S, Tighe P, Price C, Manini T, Mardini M. Developing and validating an electronic health record-based frailty index in pre-operative settings using machine learning. Journal of Intelligent Information Systems 2024;62(2):339 View
  17. Garavand A, Behmanesh A, Aslani N, Sadeghsalehi H, Ghaderzadeh M, El Kafhali S. Towards Diagnostic Aided Systems in Coronary Artery Disease Detection: A Comprehensive Multiview Survey of the State of the Art. International Journal of Intelligent Systems 2023;2023:1 View
  18. Swinnen K, Verstraete K, Baratto C, Hardy L, De Vos M, Topalovic M, Claessen G, Quarck R, Belge C, Vachiery J, Janssens W, Delcroix M. Machine learning to differentiate pulmonary hypertension due to left heart disease from pulmonary arterial hypertension. ERJ Open Research 2023;9(5):00229-2023 View
  19. Liao X, Yao C, Zhang J, Liu L. Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review. Journal of Evidence-Based Medicine 2023;16(4):534 View
  20. Sozen M, Sariyer G, Sozen M, Badhotiya G, Vijavargy L. Machine Learning Implementations for Multi-class Cardiovascular Risk Prediction in Family Health Units. International Journal of Mathematical, Engineering and Management Sciences 2023;8(6):1171 View
  21. Sun T, Wang C, Wu Y, Hsu K, Lee T. Machine learning approaches for biomarker discovery to predict large-artery atherosclerosis. Scientific Reports 2023;13(1) View
  22. Abdulazeem H, Whitelaw S, Schauberger G, Klug S, Vathy-Fogarassy Á. A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data. PLOS ONE 2023;18(9):e0274276 View
  23. Muhammad G, Naveed S, Nadeem L, Mahmood T, Khan A, Amin Y, Bahaj S. Enhancing Prognosis Accuracy for Ischemic Cardiovascular Disease Using K Nearest Neighbor Algorithm: A Robust Approach. IEEE Access 2023;11:97879 View
  24. Nabaouia L, Douzi S, Bouabid E. Explainable machine learning for coronary artery disease risk assessment and prevention. Data and Metadata 2023;2:65 View
  25. Cai Y, Cai Y, Tang L, Wang Y, Gong M, Jing T, Li H, Li-Ling J, Hu W, Yin Z, Gong D, Zhang G. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Medicine 2024;22(1) View
  26. Wu Y, Xin B, Wan Q, Ren Y, Jiang W. Risk factors and prediction models for cardiovascular complications of hypertension in older adults with machine learning: A cross-sectional study. Heliyon 2024;10(6):e27941 View
  27. Petreska A, Slavkovska D. Artificial Intelligence and Machine Learning Algorithms in Modern Cardiology. South East European Journal of Cardiology 2024;5:17 View
  28. Asif S, Zhao M, Li Y, Tang F, Ur Rehman Khan S, Zhu Y. AI-Based Approaches for the Diagnosis of Mpox: Challenges and Future Prospects. Archives of Computational Methods in Engineering 2024;31(6):3585 View
  29. Wei J, Pan B, Gan Y, Li X, Liu D, Sang B, Gao X. Temporal Relationship-Aware Treadmill Exercise Test Analysis Network for Coronary Artery Disease Diagnosis. Sensors 2024;24(9):2705 View
  30. Korial A, Gorial I, Humaidi A. An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection. Computers 2024;13(6):126 View
  31. Dubey M, Tembhurne J, Makhijani R. Improving coronary heart disease prediction with real-life dataset: a stacked generalization framework with maximum clinical attributes and SMOTE balancing for imbalanced data. Multimedia Tools and Applications 2024;83(37):85139 View
  32. Cai Y, Gong D, Tang L, Cai Y, Li H, Jing T, Gong M, Hu W, Zhang Z, Zhang X, Zhang G. Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions. Journal of Medical Internet Research 2024;26:e47645 View
  33. Botha N, Ansah E, Segbedzi C, Dumahasi V, Maneen S, Kodom R, Tsedze I, Akoto L, Atsu F. Artificial intelligent tools: evidence-mapping on the perceived positive effects on patient-care and confidentiality. BMC Digital Health 2024;2(1) View
  34. Zhou S, Blaes A, Shenoy C, Sun J, Zhang R. Risk prediction of heart diseases in patients with breast cancer: A deep learning approach with longitudinal electronic health records data. iScience 2024;27(7):110329 View
  35. Lin S, Yan H, Zhou S, Qiao Z, Chen J. HRP-OG: Online Learning with Generative Feature Replay for Hypertension Risk Prediction in a Nonstationary Environment. Sensors 2024;24(15):5033 View
  36. Omo-Okhuasuyi A, Jin Y, ElHefnawi M, Chen Y, Flores M. Multimodal Identification of Molecular Factors Linked to Severe Diabetic Foot Ulcers Using Artificial Intelligence. International Journal of Molecular Sciences 2024;25(19):10686 View
  37. Chaturvedi R, Sharma S, Narne S. Advanced Big Data Mining Techniques for Early Detection of Heart Attacks in Clinical Data. Journal for Research in Applied Sciences and Biotechnology 2023;2(3):305 View
  38. Du Z, Wang S, Yang O, He J, Yang Y, Zheng J, Zhao H, Cai Y. Machine-learning-based prediction of cardiovascular events for hyperlipidemia population with lipid variability and remnant cholesterol as biomarkers. Health Information Science and Systems 2024;12(1) View
  39. Donmez T, Kutlu M, Mansour M, Yildiz M. Explainable AI in action: a comparative analysis of hypertension risk factors using SHAP and LIME. Neural Computing and Applications 2024 View

Books/Policy Documents

  1. Brites I, Silva L, Barbosa J, Rigo S, Correia S, Leithardt V. Information Technology and Systems. View
  2. Qiao H, Chen H, Lyu J, Feng Q. Advances in Swarm Intelligence. View
  3. Meng H, Wang X. Neural Information Processing. View
  4. Srivastava R, Maji S, Panda T. Advanced Computing. View