ArticleInternational journal of environmental research and public health2020
Risk Prediction for Early Chronic Kidney Disease: Results from an Adult Health Examination Program of 19,270 Individuals.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Pooled it
- GERBEHRT: a BERT-based model tailored for German electronic health records - potential in chronic kidney disease prediction.BMC medical informatics and decision making · 2026Article
- The Comparison of Insulin Resistance Between Normal and Early Menopause Women Younger than Fifty Years Old by Machine Learning Methods.Diagnostics (Basel, Switzerland) · 2025Article
- Classification and Regression Trees analysis identifies patients at high risk for kidney function decline following hospitalization.PloS one · 2025Article
- Factors affecting disease progression in early-stage chronic kidney disease in a multi-ethnic, southeast Asian primary care population.Frontiers in medicine · 2025Article
- Variational quantum classifier-based early identification and classification of chronic kidney disease using sparse autoencoder and LASSO shrinkage.PeerJ. Computer science · 2025Article
- A Random Forest Algorithm for Assessing Risk Factors Associated With Chronic Kidney Disease: Observational Study.Asian/Pacific Island nursing journal · 2024Article
- Analyzing Longitudinal Health Screening Data with Feature Ensemble and Machine Learning Techniques: Investigating Diagnostic Risk Factors of Metabolic Syndrome for Chronic Kidney Disease Stages 3a to 3b.Diagnostics (Basel, Switzerland) · 2024Article
- Development of a machine learning tool to predict the risk of incident chronic kidney disease using health examination data.Frontiers in public health · 2024Article
- Using Machine Learning to Identify the Relationships between Demographic, Biochemical, and Lifestyle Parameters and Plasma Vitamin D Concentration in Healthy Premenopausal Chinese Women.Life (Basel, Switzerland) · 2023Article
- Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes.World journal of clinical cases · 2023Article
- Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.Journal of the Chinese Medical Association : JCMA · 2023Article
- Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese.World journal of clinical cases · 2023Article
- Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran.International journal of environmental research and public health · 2023Article
- Using Data Mining Techniques to Predict Chronic Kidney Disease: A Review Study.International journal of preventive medicine · 2023Review
- An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages.Frontiers in medicine · 2023Article
- Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening Population.Risk management and healthcare policy · 2023Article
- Assessing the length of hospital stay for patients with myasthenia gravis based on the data mining MARS approach.Frontiers in neurology · 2023Article
- Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developing effective risk prediction models is a cost-effective approach to predicting complications of chronic kidney disease (CKD) and mortality rates; however, there is inadequate evidence to support screening for CKD. In this study, four data mining algorithms, including a classification and regression tree, a C4.5 decision tree, a linear discriminant analysis, and an extreme learning machine, are used to predict early CKD. The study includes datasets from 19,270 patients, provided by an adult health examination program from 32 chain clinics and three special physical examination centers, between 2015 and 2019. There were 11 independent variables, and the glomerular filtration rate (GFR) was used as the predictive variable. The C4.5 decision tree algorithm outperformed the three comparison models for predicting early CKD based on accuracy, sensitivity, specificity, and area under the curve metrics. It is, therefore, a promising method for early CKD prediction. The experimental results showed that Urine protein and creatinine ratio (UPCR), Proteinuria (PRO), Red blood cells (RBC), Glucose Fasting (GLU), Triglycerides (TG), Total Cholesterol (T-CHO), age, and gender are important risk factors. CKD care is closely related to primary care level and is recognized as a healthcare priority in national strategy. The proposed risk prediction models can support the important influence of personality and health examination representations in predicting early CKD.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.