ArticleBioengineering (Basel, Switzerland)2022
A Machine Learning Method with Filter-Based Feature Selection for Improved Prediction of Chronic Kidney Disease.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 103 citations in OpenAlex.
- Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis.Renal failure · 2024Pooled it
- Explainable AI machine learning framework for chronic kidney disease prediction utilizing electronic health records.BMC medical informatics and decision making · 2026Review
- Dual-SwinOrd: A Dual-Head Swin Transformer with Semantic Prior Injection for Ordinal Diabetic Retinopathy Grading.Bioengineering (Basel, Switzerland) · 2026Article
- Detection and classification of medical images using deep learning for chronic kidney disease.International urology and nephrology · 2026Article
- Icariside II attenuates renal fibrosis through mTOR signaling modulation: an integrated approach combining network pharmacology, molecular dynamics, andTranslational andrology and urology · 2025Article
- 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
- Statistical consideration in nephrology research.Kidney research and clinical practice · 2025Article
- Hybrid classical and quantum computing for enhanced glioma tumor classification using TCGA data.Scientific reports · 2025Article
- Enhanced machine learning and hybrid ensemble approaches for Coronary Heart Disease prediction.PloS one · 2025Article
- Hybrid feature selection framework for enhanced credit card fraud detection using machine learning models.PloS one · 2025Article
- 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
- Novel statistically equivalent signature-based hybrid feature selection and ensemble deep learning LSTM and GRU for chronic kidney disease classification.PeerJ. Computer science · 2024Article
- Article
- Dynamic Service Function Chain Deployment and Readjustment Method Based on Deep Reinforcement Learning.Sensors (Basel, Switzerland) · 2023Article
- Chronic kidney disease prediction using boosting techniques based on clinical parameters.PloS one · 2023Article
- Machine learning-based multimodal MRI texture analysis for assessing renal function and fibrosis in diabetic nephropathy: a retrospective study.Frontiers in endocrinology · 2023Article
- Identifying Patients at Risk of Acute Kidney Injury among Patients Receiving Immune Checkpoint Inhibitors: A Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2022Article
- Machine Learning Models for the Prediction of Renal Failure in Chronic Kidney Disease: A Retrospective Cohort Study.Diagnostics (Basel, Switzerland) · 2022Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The high prevalence of chronic kidney disease (CKD) is a significant public health concern globally. The condition has a high mortality rate, especially in developing countries. CKD often go undetected since there are no obvious early-stage symptoms. Meanwhile, early detection and on-time clinical intervention are necessary to reduce the disease progression. Machine learning (ML) models can provide an efficient and cost-effective computer-aided diagnosis to assist clinicians in achieving early CKD detection. This research proposed an approach to effectively detect CKD by combining the information-gain-based feature selection technique and a cost-sensitive adaptive boosting (AdaBoost) classifier. An approach like this could save CKD screening time and cost since only a few clinical test attributes would be needed for the diagnosis. The proposed approach was benchmarked against recently proposed CKD prediction methods and well-known classifiers. Among these classifiers, the proposed cost-sensitive AdaBoost trained with the reduced feature set achieved the best classification performance with an accuracy, sensitivity, and specificity of 99.8%, 100%, and 99.8%, respectively. Additionally, the experimental results show that the feature selection positively impacted the performance of the various classifiers. The proposed approach has produced an effective predictive model for CKD diagnosis and could be applied to more imbalanced medical datasets for effective disease detection.
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.