ArticleJournal of medical systems2017
Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods.
Article in Journal of medical systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 3 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Prediction of chronic kidney disease and its progression by artificial intelligence algorithms.Journal of nephrology · 2022Pooled it
- A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock.Molecular medicine reports · 2020Pooled it
- Applications of Machine Learning for Early Diagnosis and Prognosis of Chronic Kidney Disease: Current Evidence.Diagnostics (Basel, Switzerland) · 2026Review
- Exploratory eye movement characteristics to aid screening of schizophrenia and bipolar disorder: A cross-sectional outpatient study.Scientific reports · 2026Article
- Development and validation of an inflammatory index-based interpretable machine learning model for mortality risk stratification in hemodialysis patients.European journal of medical research · 2026Article
- Association Between New Tea Drink Consumption and Mental Health Disorders Among Chinese College Students: An Interpretable Machine Learning Approach.Psychology research and behavior management · 2026Article
- Stacking ensemble model for predicting chronic kidney disease in the Uddanam region of India with unknown etiology.Scientific reports · 2025Article
- Optimized machine learning based comparative analysis of predictive models for classification of kidney tumors.Scientific reports · 2025Article
- Visualizing Relaxation in Wearables: Multi-Domain Feature Fusion of HRV Using Fuzzy Recurrence Plots.Sensors (Basel, Switzerland) · 2025Article
- Improved CKD classification based on explainable artificial intelligence with extra trees and BBFS.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
- The iterated score regression estimation algorithm for PCA-based missing data with high correlation.Scientific reports · 2025Article
- Machine learning assisted breathomic approach for early-stage thoracic cancer detection.Frontiers in oncology · 2025Article
- Cloud-based real-time enhancement for disease prediction using Confluent Cloud, Apache Kafka, feature optimization, and explainable artificial intelligence.PeerJ. Computer science · 2025Article
- Investigation on explainable machine learning models to predict chronic kidney diseases.Scientific reports · 2024Article
- Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research.Diagnostics (Basel, Switzerland) · 2024Review
- A hybrid feature selection algorithm combining information gain and grouping particle swarm optimization for cancer diagnosis.PloS one · 2024Article
- Enhancing machine learning-based forecasting of chronic renal disease with explainable AI.PeerJ. Computer science · 2024Article
- A comparative investigation of machine learning algorithms for predicting safety signs comprehension based on socio-demographic factors and cognitive sign features.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.
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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.