ArticleDiagnostics (Basel, Switzerland)2022
A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 231 citations in OpenAlex.
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis.Renal failure · 2024Pooled it
- Applications of Machine Learning for Early Diagnosis and Prognosis of Chronic Kidney Disease: Current Evidence.Diagnostics (Basel, Switzerland) · 2026Review
- Explainable AI machine learning framework for chronic kidney disease prediction utilizing electronic health records.BMC medical informatics and decision making · 2026Review
- Detection and classification of medical images using deep learning for chronic kidney disease.International urology and nephrology · 2026Article
- PCSA-Net: pyramid channel and spatial attention network for multiclass renal disease diagnosis using CT images.Scientific reports · 2026Article
- Predictive Role of Hematological Biomarkers in Chronic Kidney Disease Progression.Journal of clinical laboratory analysis · 2026Article
- Kidney cancer diagnosis and surgery selection by double decker convolutional neural network from CT scans combined with great wall construction algorithm.Abdominal radiology (New York) · 2025Article
- A population based optimization of convolutional neural networks for chronic kidney disease prediction.Scientific reports · 2025Article
- Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions.International urology and nephrology · 2025Review
- Kidney Disease Segmentation and Classification Using Firefly Sigma Seeker and MagWeight Rank Techniques.Bioengineering (Basel, Switzerland) · 2025Article
- Early prediction of CKD from time series data using adaptive PSO optimized echo state networks.Scientific reports · 2025Article
- Observational
- Article
- AI-Powered Clinical Decision Support Systems in Disease Diagnosis, Treatment Planning, and Prognosis: A Systematic Review.Medical journal of the Islamic Republic of Iran · 2025Review
- Development of risk models for early detection and prediction of chronic kidney disease in clinical settings.Scientific reports · 2024Article
- Deep-kidney: an effective deep learning framework for chronic kidney disease prediction.Health information science and systems · 2024Article
- Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate.Scientific reports · 2024Article
- Integrating neural networks with advanced optimization techniques for accurate kidney disease diagnosis.Scientific reports · 2024Article
- An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection.International urology and nephrology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes and high blood pressure are the primary causes of Chronic Kidney Disease (CKD). Glomerular Filtration Rate (GFR) and kidney damage markers are used by researchers around the world to identify CKD as a condition that leads to reduced renal function over time. A person with CKD has a higher chance of dying young. Doctors face a difficult task in diagnosing the different diseases linked to CKD at an early stage in order to prevent the disease. This research presents a novel deep learning model for the early detection and prediction of CKD. This research objectives to create a deep neural network and compare its performance to that of other contemporary machine learning techniques. In tests, the average of the associated features was used to replace all missing values in the database. After that, the neural network's optimum parameters were fixed by establishing the parameters and running multiple trials. The foremost important features were selected by Recursive Feature Elimination (RFE). Hemoglobin, Specific Gravity, Serum Creatinine, Red Blood Cell Count, Albumin, Packed Cell Volume, and Hypertension were found as key features in the RFE. Selected features were passed to machine learning models for classification purposes. The proposed Deep neural model outperformed the other four classifiers (Support Vector Machine (SVM),
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.