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Updated: Jun 24, 2025
Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
A Random Forest Algorithm for Assessing Risk Factors Associated With Chronic Kidney Disease: Observational Study
Pei Liu1, Yijun Liu2, Hao Liu3
1Department of Mathematics and Physics, Second Military Medical University, Shanghai, China.
Insights
The random forest algorithm effectively identifies chronic kidney disease (CKD) risk factors, including age and albuminuria. This machine learning approach aids in early CKD detection and intervention, crucial for managing this growing global health issue.
Area of Science:
- Medical research
- Public health
- Machine learning applications in healthcare
Background:
- Chronic kidney disease (CKD) prevalence and mortality are rising globally.
- CKD poses a significant and increasing economic burden.
- Early detection and intervention are vital for mitigating CKD progression and patient costs.
Purpose of the Study:
- To investigate the utility of the random forest (RF) algorithm for assessing CKD risk factors.
- To compare RF algorithm performance against traditional logistic regression models.
Main Methods:
- Analysis of 40,686 individuals' screening records (2015-2020) in Shanghai, China.
- Classification of participants based on glomerular filtration rate and albuminuria.
- Application of logistic regression and RF algorithms to identify and rank CKD risk factors.
Main Results:
- Logistic regression identified gender, age, obesity, abnormal eGFR, retirement, and insurance status as significant CKD risk factors.
- RF algorithm highlighted age, albuminuria, working status, and urine albumin-creatinine ratio as top predictors.
- The RF model achieved a high predictive accuracy with an AUC of 93.15%.
Conclusions:
- The RF algorithm demonstrates significant predictive value for CKD risk factor assessment.
- RF enables effective screening of individuals at risk for CKD.
- These findings support the use of RF for early CKD intervention and prevention strategies.
Background:
The prevalence and mortality rate of chronic kidney disease (CKD) are increasing year by year, and it has become a global public health issue. The economic burden caused by CKD is increasing at a rate of 1% per year. CKD is highly prevalent and its treatment cost is high but unfortunately remains unknown. Therefore, early detection and intervention are vital means to mitigate the treatment burden on patients and decrease disease progression.
Objective:
In this study, we investigated the advantages of using the random forest (RF) algorithm for assessing risk factors associated with CKD.
Methods:
We included 40,686 people with complete screening records who underwent screening between January 1, 2015, and December 22, 2020, in Jing'an District, Shanghai, China. We grouped the participants into those with and those without CKD by staging based on the glomerular filtration rate staging and grouping based on albuminuria. Using a logistic regression model, we determined the relationship between CKD and risk factors. The RF machine learning algorithm was used to score the predictive variables and rank them based on their importance to construct a prediction model.
Results:
The logistic regression model revealed that gender, older age, obesity, abnormal index estimated glomerular filtration rate, retirement status, and participation in urban employee medical insurance were significantly associated with the risk of CKD. On RF algorithm-based screening, the top 4 factors influencing CKD were age, albuminuria, working status, and urinary albumin-creatinine ratio. The RF model predicted an area under the receiver operating characteristic curve of 93.15%.
Conclusions:
Our findings reveal that the RF algorithm has significant predictive value for assessing risk factors associated with CKD and allows the screening of individuals with risk factors. This has crucial implications for early intervention and prevention of CKD.
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