Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Factors Affecting Renal Clearance: Renal Impairment01:17

Factors Affecting Renal Clearance: Renal Impairment

84
Renal dysfunction significantly impairs the renal clearance of drugs, leading to potential complications in drug therapy. Renal failure, which can be caused by various factors, poses a significant challenge in the elimination of drugs from the body.
One condition associated with renal failure is uremia. Uremia is characterized by impaired glomerular filtration and fluid accumulation in the body. This condition hinders the renal clearance of drugs, resulting in drug accumulation and potential...
84
Relative Risk01:12

Relative Risk

150
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
150
Renal Failure: Dose Adjustments01:11

Renal Failure: Dose Adjustments

84
In patients with renal impairment, drugs undergo significant changes in their pharmacokinetics, which require dosage adjustments to ensure safe and effective therapy.
Reduced renal clearance and elimination rate are common outcomes of renal impairment. These alterations lead to a prolonged elimination half-life and an altered apparent volume of distribution for drugs. As a result, dosage adjustments are typically necessary to maintain optimal drug levels in the body.
However, dosage adjustments...
84
Odds Ratio01:09

Odds Ratio

127
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
127
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

125
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
125
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

137
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
137

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Suicidal Thoughts and Behaviors Among Chinese Adolescents in Relation to Negative Life Events, Internet Addiction, and Sexual Abuse: Cross-Sectional Study.

Journal of medical Internet research·2026
Same author

Decoding sexually dimorphic proteomic landscapes in the context of aging and mortality.

Communications medicine·2025
Same author

The Influence of Different Kidney Replacement Modalities on Health-Related Quality of Life in Patients with ESKD.

Kidney360·2025
Same author

Association of depressive symptoms with non-fatal cardiovascular disease in middle-aged and elderly patients with hypertension: a cohort study from China.

BMJ open·2025
Same author

Body mass index and physical training-related injuries in military personnel: a systematic review and meta-regression analysis.

BMJ military health·2025
Same author

Decomposition Analysis of the Prevalence of Denture Use Between Rural and Urban Older Individuals With Edentulism in China: Cross-Sectional Study.

Interactive journal of medical research·2024

Related Experiment Video

Updated: Jun 24, 2025

Assessment of Vascular Function in Patients With Chronic Kidney Disease
08:50

Assessment of Vascular Function in Patients With Chronic Kidney Disease

Published on: June 16, 2014

16.2K

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.

Asian/Pacific Island Nursing Journal
|June 3, 2024
PubMed
Summary

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.

Keywords:
assessmentchronic kidney diseaserandom forest modelrisk factors

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

Related Experiment Videos

Last Updated: Jun 24, 2025

Assessment of Vascular Function in Patients With Chronic Kidney Disease
08:50

Assessment of Vascular Function in Patients With Chronic Kidney Disease

Published on: June 16, 2014

16.2K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

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.