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Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease

Alyousef, AA; Nihtyanova, S; Denton, C; Bosoni, P; Bellazzi, R; Tucker, A; (2018) Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease. Journal of Healthcare Informatics Research , 2 (4) pp. 402-422. 10.1007/s41666-018-0029-6. Green open access

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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.

Type: Article
Title: Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s41666-018-0029-6
Publisher version: https://doi.org/10.1007/s41666-018-0029-6
Language: English
Additional information: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Keywords: Consensus clustering, Disease subgroup discovery, Classification
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Inflammation
URI: https://discovery.ucl.ac.uk/id/eprint/10106781
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