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

Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (997) New Methods (1134) Epidemiology (196) Nephrology/Renal Disorders (99)

Published on in Vol 21, No 12 (2019): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14204, first published .
Detecting Lifestyle Risk Factors for Chronic Kidney Disease With Comorbidities: Association Rule Mining Analysis of Web-Based Survey Data

Detecting Lifestyle Risk Factors for Chronic Kidney Disease With Comorbidities: Association Rule Mining Analysis of Web-Based Survey Data

Detecting Lifestyle Risk Factors for Chronic Kidney Disease With Comorbidities: Association Rule Mining Analysis of Web-Based Survey Data

Authors of this article:

Suyuan Peng1, 2 Author Orcid Image ;   Feichen Shen2 Author Orcid Image ;   Andrew Wen2 Author Orcid Image ;   Liwei Wang2 Author Orcid Image ;   Yadan Fan3 Author Orcid Image ;   Xusheng Liu4 Author Orcid Image ;   Hongfang Liu2 Author Orcid Image
Article Authors Cited by (22) Tweetations (5) Metrics

Journals

  1. Ko K, Lee C, Nam S, Ahn S, Bae J, Ban C, Yoo J, Park J, Han H. Epidemiological Characterization of a Directed and Weighted Disease Network Using Data From a Cohort of One Million Patients: Network Analysis. Journal of Medical Internet Research 2020;22(4):e15196 View
  2. Maduako I, Gong Y, Wachowicz M. Building k‐partite association graphs for finding recommendation patterns from questionnaire data. Transactions in GIS 2021;25(5):2641 View
  3. Wang Y, Sun Y, Lu N, Feng X, Gao M, Zhang L, Dou Y, Meng F, Zhang K, Khalaf O. Diagnosis and Treatment Rules of Chronic Kidney Disease and Nursing Intervention Models of Related Mental Diseases Using Electronic Medical Records and Data Mining. Journal of Healthcare Engineering 2021;2021:1 View
  4. Sarker M, Moriyama M, Rashid H, Rahman M, Chisti M, Das S, Jahan Y, Saha S, Arifeen S, Ahmed T, Faruque A. Health Education Through a Campaign and mHealth to Enhance Knowledge and Quality of Life Among Patients With Chronic Kidney Disease in Bangladesh: Protocol for a Randomized Controlled Trial. JMIR Research Protocols 2021;10(11):e30191 View
  5. Akimoto T, Miyake S, Suzuki R, Iida Y, Shimizu N, Manaka H, Nakai Y, Sakata K, Yamamoto T. Safety of coil embolisation in small (smaller than 5 mm) unruptured intracranial aneurysms: A retrospective multicentre analysis. Interdisciplinary Neurosurgery 2023;31:101678 View
  6. Kim P, Kim C, Lee S, Shon J, Kwon Y, Kim J, Kim D, Yu H, Ahn H, Jeon J, Kim Y, Lee J. Another Look at Obesity Paradox in Acute Ischemic Stroke: Association Rule Mining. Journal of Personalized Medicine 2021;12(1):16 View
  7. Chen F, Pongpirul K. Rethink nutritional management in chronic kidney disease care. Frontiers in Nephrology 2023;3 View
  8. Wen H, Yang D, Xie C, Shi F, Liu Y, Zhang J, Yu C. Comparison of trend in chronic kidney disease burden between China, Japan, the United Kingdom, and the United States. Frontiers in Public Health 2022;10 View
  9. Du Y, Dennis B, Ramirez V, Li C, Wang J, Meireles C. Experiences and disease self-management in individuals living with chronic kidney disease: qualitative analysis of the National Kidney Foundation’s online community. BMC Nephrology 2022;23(1) View
  10. Kuma A, Kato A. Lifestyle-Related Risk Factors for the Incidence and Progression of Chronic Kidney Disease in the Healthy Young and Middle-Aged Population. Nutrients 2022;14(18):3787 View
  11. Lightfoot C, Wilkinson T, Yates T, Davies M, Smith A. ‘Self-Management Intervention through Lifestyle Education for Kidney health’ (the SMILE-K study): protocol for a single-blind longitudinal randomised controlled trial with nested pilot study. BMJ Open 2022;12(11):e064916 View
  12. Eisenga M, Mayer G, Pirklbauer M, Provenzano M. Editorial: Personalized medicine in CKD patients. Frontiers in Nephrology 2023;3 View
  13. Lindeback R, Abdo R, Schnabel L, Le Jambre R, Kennedy S, Katz T, Ooi C, Lambert K. Does the Nutritional Intake and Diet Quality of Children With Chronic Kidney Disease Differ From Healthy Controls? A Comprehensive Evaluation. Journal of Renal Nutrition 2024;34(4):283 View
  14. Liu X, Chen G, Wen S, Zuo W. Effective approaches for mining correlated and low-average-cost patterns. Knowledge-Based Systems 2024;302:112376 View
  15. El Moneem Elfedawy M, El Sadek Elsebai S, Tawfik H, Youness E, Zaki M. Adropin a candidate diagnostic biomarker for cardiovascular disease in patients with chronic kidney disease. Journal of Genetic Engineering and Biotechnology 2024;22(4):100438 View
  16. M J, N D. Conceptual metaphor quantum correlation and radial basis extreme learning for predicting chronic kidney disease. Computers and Electrical Engineering 2025;122:109933 View
  17. Islam M, Poly T, Okere A, Wang Y. Explainable machine learning model incorporating social determinants of health to predict chronic kidney disease in type 2 diabetes patients. Journal of Diabetes & Metabolic Disorders 2025;24(1) View
  18. Dastani M, Ghorbani M, Eskandarioun M, Hassani Goodarzi T, Torabian A, Talebnia S. Association Rule Mining Analysis of Cardiovascular Risk Factors in the CDC Diabetes Health Indicators Dataset. InfoScience Trends 2025;2(6):73 View

Conference Proceedings

  1. Amini F, Oktora S. THE 2ND SCIENCE AND MATHEMATICS INTERNATIONAL CONFERENCE (SMIC 2020): Transforming Research and Education of Science and Mathematics in the Digital Age. Comorbid of chronic kidney disease (CKD) patients who undergoing dialysis in Indonesia using firth logistic regression View
  2. Qi J, Fu W. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). Design of Cluster Data Association Mining Algorithm Based on Multi-GANs View
  3. Wang J, Cornely P. 2023 6th International Conference on Computing and Big Data (ICCBD). Data-Driven Decisions: Exploring the Impact of Data Mining in Healthcare View
  4. Nimmala S, Mahendar M, Manasa P, Lakshmi H, Asha Kiran M, Raghavendra C. 2024 4th International Conference on Soft Computing for Security Applications (ICSCSA). Fuzzy-Enhanced XGBoost Model for Classifying Kidney Disease Severity View