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

10.2196/14204 ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. e14204 ◽  
Author(s):  
Suyuan Peng ◽  
Feichen Shen ◽  
Andrew Wen ◽  
Liwei Wang ◽  
Yadan Fan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Association Rule ◽  
Chronic Conditions ◽  
Association Rule Mining ◽  
Lifestyle Modification ◽  
Rule Mining ◽  
Web Based ◽  
Lifestyle Risk Factors ◽  
Lifestyle Choices ◽  
Lifestyle Risk

Background The rise in the number of patients with chronic kidney disease (CKD) and consequent end-stage renal disease necessitating renal replacement therapy has placed a significant strain on health care. The rate of progression of CKD is influenced by both modifiable and unmodifiable risk factors. Identification of modifiable risk factors, such as lifestyle choices, is vital in informing strategies toward renoprotection. Modification of unhealthy lifestyle choices lessens the risk of CKD progression and associated comorbidities, although the lifestyle risk factors and modification strategies may vary with different comorbidities (eg, diabetes, hypertension). However, there are limited studies on suitable lifestyle interventions for CKD patients with comorbidities. Objective The objectives of our study are to (1) identify the lifestyle risk factors for CKD with common comorbid chronic conditions using a US nationwide survey in combination with literature mining, and (2) demonstrate the potential effectiveness of association rule mining (ARM) analysis for the aforementioned task, which can be generalized for similar tasks associated with noncommunicable diseases (NCDs). Methods We applied ARM to identify lifestyle risk factors for CKD progression with comorbidities (cardiovascular disease, chronic pulmonary disease, rheumatoid arthritis, diabetes, and cancer) using questionnaire data for 450,000 participants collected from the Behavioral Risk Factor Surveillance System (BRFSS) 2017. The BRFSS is a Web-based resource, which includes demographic information, chronic health conditions, fruit and vegetable consumption, and sugar- or salt-related behavior. To enrich the BRFSS questionnaire, the Semantic MEDLINE Database was also mined to identify lifestyle risk factors. Results The results suggest that lifestyle modification for CKD varies among different comorbidities. For example, the lifestyle modification of CKD with cardiovascular disease needs to focus on increasing aerobic capacity by improving muscle strength or functional ability. For CKD patients with chronic pulmonary disease or rheumatoid arthritis, 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.

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

2019 ◽  
Author(s):  
Suyuan Peng ◽  
Feichen Shen ◽  
Andrew Wen ◽  
Liwei Wang ◽  
Yadan Fan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Association Rule ◽  
Chronic Conditions ◽  
Association Rule Mining ◽  
Lifestyle Modification ◽  
Rule Mining ◽  
Web Based ◽  
Lifestyle Risk Factors ◽  
Lifestyle Choices ◽  
Lifestyle Risk

BACKGROUND The rise in the number of patients with chronic kidney disease (CKD) and consequent end-stage renal disease necessitating renal replacement therapy has placed a significant strain on health care. The rate of progression of CKD is influenced by both modifiable and unmodifiable risk factors. Identification of modifiable risk factors, such as lifestyle choices, is vital in informing strategies toward renoprotection. Modification of unhealthy lifestyle choices lessens the risk of CKD progression and associated comorbidities, although the lifestyle risk factors and modification strategies may vary with different comorbidities (eg, diabetes, hypertension). However, there are limited studies on suitable lifestyle interventions for CKD patients with comorbidities. OBJECTIVE The objectives of our study are to (1) identify the lifestyle risk factors for CKD with common comorbid chronic conditions using a US nationwide survey in combination with literature mining, and (2) demonstrate the potential effectiveness of association rule mining (ARM) analysis for the aforementioned task, which can be generalized for similar tasks associated with noncommunicable diseases (NCDs). METHODS We applied ARM to identify lifestyle risk factors for CKD progression with comorbidities (cardiovascular disease, chronic pulmonary disease, rheumatoid arthritis, diabetes, and cancer) using questionnaire data for 450,000 participants collected from the Behavioral Risk Factor Surveillance System (BRFSS) 2017. The BRFSS is a Web-based resource, which includes demographic information, chronic health conditions, fruit and vegetable consumption, and sugar- or salt-related behavior. To enrich the BRFSS questionnaire, the Semantic MEDLINE Database was also mined to identify lifestyle risk factors. RESULTS The results suggest that lifestyle modification for CKD varies among different comorbidities. For example, the lifestyle modification of CKD with cardiovascular disease needs to focus on increasing aerobic capacity by improving muscle strength or functional ability. For CKD patients with chronic pulmonary disease or rheumatoid arthritis, 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.

scholarly journals Stroke secondary prevention, a non-surgical and non-pharmacological consensus definition: results of a Delphi study

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Maggie Lawrence ◽  
Eric Asaba ◽  
Elaine Duncan ◽  
Marie Elf ◽  
Gunilla Eriksson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Secondary Prevention ◽  
Delphi Study ◽  
Lifestyle Modification ◽  
Well Being ◽  
Evidence Base ◽  
Consensus Definition ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk ◽  
Definition Of

Abstract Objective Evidence supporting lifestyle modification in vascular risk reduction is limited, drawn largely from primary prevention studies. To advance the evidence base for non-pharmacological and non-surgical stroke secondary prevention (SSP), empirical research is needed, informed by a consensus-derived definition of SSP. To date, no such definition has been published. We used Delphi methods to generate an evidence-based definition of non-pharmacological and non-surgical SSP. Results The 16 participants were members of INSsPiRE (International Network of Stroke Secondary Prevention Researchers), a multidisciplinary group of trialists, academics and clinicians. The Elicitation stage identified 49 key elements, grouped into 3 overarching domains: Risk factors, Education, and Theory before being subjected to iterative stages of elicitation, ranking, discussion, and anonymous voting. In the Action stage, following an experience-based engagement with key stakeholders, a consensus-derived definition, complementing current pharmacological and surgical SSP pathways, was finalised: Non-pharmacological and non-surgical stroke secondary prevention supports and improves long-term health and well-being in everyday life and reduces the risk of another stroke, by drawing from a spectrum of theoretically informed interventions and educational strategies. Interventions to self-manage modifiable lifestyle risk factors are contextualized and individualized to the capacities, needs, and personally meaningful priorities of individuals with stroke and their families.

scholarly journals Development of Multimorbidity Over Time: An Analysis of Belgium Primary Care Data Using Markov Chains and Weighted Association Rule Mining

2020 ◽  
Author(s):  
Xi Shi ◽  
Gorana Nikolic ◽  
Gijs Van Pottelbergh ◽  
Marjan van den Akker ◽  
Rein Vos ◽  
...  
Keyword(s):  
Health Care ◽  
Markov Chains ◽  
Association Rule ◽  
Chronic Conditions ◽  
Association Rule Mining ◽  
Musculoskeletal Diseases ◽  
Current Knowledge ◽  
Health Care Systems ◽  
Rule Mining ◽  
Care Systems

Abstract Background The prevalence of multimorbidity is increasing in recent years, and patients with multimorbidity often have a decrease in quality of life and require more health care. The aim of this study was to explore the evolution of multimorbidity taking the sequence of diseases into consideration. Methods We used a Belgian database collected by extracting coded parameters and more than 100 chronic conditions from the Electronic Health Records of general practitioners to study patients older than 40 years with multiple diagnoses between 1991 and 2015 (N = 65 939). We applied Markov chains to estimate the probability of developing another condition in the next state after a diagnosis. The results of Weighted Association Rule Mining (WARM) allow us to show strong associations among multiple conditions. Results About 66.9% of the selected patients had multimorbidity. Conditions with high prevalence, such as hypertension and depressive disorder, were likely to occur after the diagnosis of most conditions. Patterns in several disease groups were apparent based on the results of both Markov chain and WARM, such as musculoskeletal diseases and psychological diseases. Psychological diseases were frequently followed by irritable bowel syndrome. Conclusions Our study used Markov chains and WARM for the first time to provide a comprehensive view of the relations among 103 chronic conditions, taking sequential chronology into consideration. Some strong associations among specific conditions were detected and the results were consistent with current knowledge in literature, meaning the approaches were valid to be used on larger data sets, such as National Health care Systems or private insurers.

scholarly journals Another Look at Obesity Paradox in Acute Ischemic Stroke: Association Rule Mining

2021 ◽  
Vol 12 (1) ◽  
pp. 16
Author(s):  
Pum-Jun Kim ◽  
Chulho Kim ◽  
Sang-Hwa Lee ◽  
Jong-Hee Shon ◽  
Youngsuk Kwon ◽  
...  
Keyword(s):  
Risk Factors ◽  
Ischemic Stroke ◽  
Beneficial Effect ◽  
Acute Ischemic Stroke ◽  
Association Rule ◽  
Association Rule Mining ◽  
Obesity Paradox ◽  
Stroke Severity ◽  
Obese Patients ◽  
Rule Mining

Though obesity is generally associated with the development of cardiovascular disease (CVD) risk factors, previous reports have also reported that obesity has a beneficial effect on CVD outcomes. We aimed to verify the existing obesity paradox through binary logistic regression (BLR) and clarify the paradox via association rule mining (ARM). Patients with acute ischemic stroke (AIS) were assessed for their 3-month functional outcome using the modified Rankin Scale (mRS) score. Predictors for poor outcome (mRS 3–6) were analyzed through BLR, and ARM was performed to find out which combination of risk factors was concurrently associated with good outcomes using maximal support, confidence, and lift values. Among 2580 patients with AIS, being obese (OR [odds ratio], 0.78; 95% CI, 0.62–0.99) had beneficial effects on the outcome at 3 months in BLR analysis. In addition, the ARM algorithm showed obese patients with good outcomes were also associated with an age less than 55 years and mild stroke severity. While BLR analysis showed a beneficial effect of obesity on stroke outcome, in ARM analysis, obese patients had a relatively good combination of risk factor profiles compared to normal BMI patients. These results may partially explain the obesity paradox phenomenon in AIS patients.

Using association rule mining to identify risk factors for early childhood caries

2015 ◽  
Vol 122 (2) ◽  
pp. 175-181 ◽  
Author(s):  
Vladimir Ivančević ◽  
Ivan Tušek ◽  
Jasmina Tušek ◽  
Marko Knežević ◽  
Salaheddin Elheshk ◽  
...  
Keyword(s):  
Risk Factors ◽  
Early Childhood ◽  
Association Rule ◽  
Association Rule Mining ◽  
Early Childhood Caries ◽  
Rule Mining ◽  
Childhood Caries

ANALYSIS OF MESENCHYMAL STEM CELL DIFFERENTIATION IN VITRO USING CLASSIFICATION ASSOCIATION RULE MINING

2009 ◽  
Vol 07 (06) ◽  
pp. 905-930 ◽  
Author(s):  
WEIQI WANG ◽  
YANBO JUSTIN WANG ◽  
RENÉ BAÑARES-ALCÁNTARA ◽  
FRANS COENEN ◽  
ZHANFENG CUI
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Stem Cell Differentiation ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Web Based ◽  
Mesenchymal Stem Cell Differentiation ◽  
Classification Prediction

In this paper, data mining is used to analyze the data on the differentiation of mammalian Mesenchymal Stem Cells (MSCs), aiming at discovering known and hidden rules governing MSC differentiation, following the establishment of a web-based public database containing experimental data on the MSC proliferation and differentiation. To this effect, a web-based public interactive database comprising the key parameters which influence the fate and destiny of mammalian MSCs has been constructed and analyzed using Classification Association Rule Mining (CARM) as a data-mining technique. The results show that the proposed approach is technically feasible and performs well with respect to the accuracy of (classification) prediction. Key rules mined from the constructed MSC database are consistent with experimental observations, indicating the validity of the method developed and the first step in the application of data mining to the study of MSCs.

scholarly journals An Association Rule Mining Analysis of Lifestyle Behavioral Risk Factors in Cancer Survivors with High Cardiovascular Disease Risk

2021 ◽  
Vol 11 (5) ◽  
pp. 366
Author(s):  
Su Jung Lee ◽  
Kathleen B. Cartmell
Keyword(s):  
Cardiovascular Disease ◽  
Cancer Survivors ◽  
Risk Behaviors ◽  
Association Rule ◽  
Odds Ratio ◽  
Association Rule Mining ◽  
Risk Group ◽  
Rule Mining ◽  
Current Smoking ◽  
Lifestyle Risk

We aimed to assess which lifestyle risk behaviors have the greatest influence on the risk of cardiovascular disease in cancer survivors and which of these behaviors are most prominently clustered in cancer survivors, using logistic regression and association rule mining (ARM). We analyzed a consecutive series of 897 cancer survivors from the Korean National Health and Nutritional Exam Survey (2012–2016). Cardiovascular disease risks were assessed using the atherosclerotic cardiovascular disease score (ASCVDs). We classified participants as being in a low-risk group if their calculated ASCVDs was less than 10% and as being in a high-risk group if their score was 10% or higher. We used association rule mining to analyze patterns of lifestyle risk behaviors by ASCVDs risk group, based upon public health recommendations described in the Alameda 7 health behaviors (current smoking, heavy drinking, physical inactivity, obesity, breakfast skipping, frequent snacking, and suboptimal sleep duration). Forty-two percent of cancer survivors had a high ASCVD. Current smoking (common odds ratio, 11.19; 95% confidence interval, 3.66–34.20, p < 0.001) and obesity (common odds ratio, 2.67; 95% confidence interval, 1.40–5.08, p < 0.001) were significant predictors of high ASCVD in cancer survivors within a multivariate model. In ARM analysis, current smoking and obesity were identified as important lifestyle risk behaviors in cancer survivors. In addition, various lifestyle risk behaviors co-occurred with smoking in male cancer survivors.

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