ArticleJournal of healthcare informatics research2018
Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease.
Article in Journal of healthcare informatics research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Machine perfusion distribution across clinical phenotypes in kidney transplantation: A national cohort study using unsupervised clustering.World journal of transplantation · 2026Article
- Predicting fracture risk in Y-TZP zirconia implants using radiomics-based artificial intelligence.BMC oral health · 2026Article
- Development and validation of liquid-liquid phase separation-associated prognostic biomarkers in gastric cancer.Discover oncology · 2025Article
- An Ensemble Machine Learning Model for Early Prediction of Vancomycin-Induced Acute Kidney Injury in ICU Patients.Archives of academic emergency medicine · 2025Article
- A review of model evaluation metrics for machine learning in genetics and genomics.Frontiers in bioinformatics · 2024Review
- Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering.Medicines (Basel, Switzerland) · 2023Article
- Distinct phenotypes of kidney transplant recipients aged 80 years or older in the USA by machine learning consensus clustering.BMJ surgery, interventions, & health technologies · 2023Article
- Neutrophil extracellular traps-related signature predicts the prognosis and immune infiltration in gastric cancer.Frontiers in medicine · 2023Article
- Distinct clinical profiles and post-transplant outcomes among kidney transplant recipients with lower education levels: uncovering patterns through machine learning clustering.Renal failure · 2023Article
- Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering.Medicina (Kaunas, Lithuania) · 2022Article
- Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States.Journal of clinical medicine · 2022Article
- Distinct Phenotypes of Kidney Transplant Recipients in the United States with Limited Functional Status as Identified through Machine Learning Consensus Clustering.Journal of personalized medicine · 2022Article
- Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes.Transplant international : official journal of the European Society for Organ Transplantation · 2022Article
- The cancer-associated fibroblast-related signature predicts prognosis and indicates immune microenvironment infiltration in gastric cancer.Frontiers in immunology · 2022Article
- Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia.Diagnostics (Basel, Switzerland) · 2021Article
- Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units.Journal of personalized medicine · 2021Article
- Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements.Journal of clinical medicine · 2021Article
- Development and Validation of a Magnetic Resonance Imaging-Based Machine Learning Model for TMJ Pathologies.BioMed research international · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Disease subtyping, which helps to develop personalized treatments, remains a challenge in data analysis because of the many different ways to group patients based upon their data. However, if we can identify subclasses of disease, then it will help to develop better models that are more specific to individuals and should therefore improve prediction and understanding of the underlying characteristics of the disease in question. This paper proposes a new algorithm that integrates consensus clustering methods with classification in order to overcome issues with sample bias. The new algorithm combines K-means with consensus clustering in order build cohort-specific decision trees that improve classification as well as aid the understanding of the underlying differences of the discovered groups. The methods are tested on a real-world freely available breast cancer dataset and data from a London hospital on systemic sclerosis, a rare potentially fatal condition. Results show that "nearest consensus clustering classification" improves the accuracy and the prediction significantly when this algorithm has been compared with competitive similar 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.