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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Mar 30, 2019
Open Peer Review Period: Apr 1, 2019 - Apr 8, 2019
Date Accepted: Oct 22, 2019
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

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

Peng S, Shen F, Wen A, Wang L, Fan Y, Liu X, Liu H

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

J Med Internet Res 2019;21(12):e14204

DOI: 10.2196/14204

PMID: 31821152

PMCID: 6930505

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

  • Suyuan Peng; 
  • Feichen Shen; 
  • Andrew Wen; 
  • Liwei Wang; 
  • Yadan Fan; 
  • Xusheng Liu; 
  • Hongfang Liu

ABSTRACT

Background:

The rise in the number of patients with chronic kidney disease (CKD) and consequent end-stage renal disease (ESRD) necessitating renal replacement therapy has placed significant strain upon healthcare. The burden of CKD is substantial. According to WHO global health estimates, 864,226 deaths (or 1.5% of deaths worldwide) were attributable to this condition in 2012. The rate of progression of CKD is influenced by both modifiable and unmodifiable risk factors. Identification of modifiable risk factors such as lifestyle choices has been vital in informing strategies towards renoprotection as modification of unhealthy lifestyle choices lessens the risk of CKD progression and associated comorbidities. However, the lifestyle risk factors and modification strategies may vary due to different comorbidities and studies on suitable lifestyle interventions for CKD patients with comorbidities are sparse.

Objective:

The objective of our study aims to (1) identify the lifestyle risk factors for CKD with common comorbid chronic conditions using an US’ nationwide survey in combination with literature mining; (2) demonstrate the potential effectiveness of association rule mining (ARM) analysis for the aforementioned task which can be generalized for similar tasks associated with non-communicable diseases (NCDs).

Methods:

We applied ARM to identify lifestyle risk factors for CKD progression with comorbidities using questionnaire data for 450,000 participants collected from the Behavioral Risk Factor Surveillance System (BRFSS) 2017, a web-based resource, including demographic information, chronic health conditions, fruit and vegetable consumption, sugar or salt-related behavior, among others. To enrich the BRFSS questionnaire, the Semantic Medline Database (SemMedDB) was also mined to identify lifestyle risk factors.

Results:

The results suggest that the lifestyle modification of CKD varies among different comorbidities. For example, the lifestyle modification of CKD with cardiovascular disease (CVD) needs to focus on increasing aerobic capacity by improving muscle strength or functional ability. For CKD patients with chronic pulmonary disease (CPD) or rheumatoid arthritis (RA), lifestyle modification should be high dietary fiber intake and participation in moderate-intensity exercise. Meanwhile, the management of CKD patients with diabetes focuses on exercise and weight loss predominantly.

Conclusions:

We have demonstrated the use of ARM to identify lifestyle risk factors for CKD with common comorbid chronic conditions using data from BRFSS 2017. Our methods can be generalized to advance chronic disease management with more focused and optimized lifestyle modification of NCDs.


 Citation

Please cite as:

Peng S, Shen F, Wen A, Wang L, Fan Y, Liu X, Liu H

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

J Med Internet Res 2019;21(12):e14204

DOI: 10.2196/14204

PMID: 31821152

PMCID: 6930505

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