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Latent class multi-label classification to identify subclasses of disease for improved prediction

Alyousef, AA; Nihtyanova, S; Denton, CP; Bosoni, P; Bellazzi, R; Tucker, A; (2019) Latent class multi-label classification to identify subclasses of disease for improved prediction. In: Proceedings of the 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS). (pp. pp. 535-538). IEEE Green open access

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Abstract

Disease subtyping can assist the development of precision medicine but remains a challenge in data analysis by reason of the many different methods to group individuals depending on their data. However, identification of subclasses of disease will help to produce better models which are more specific to patients and will improve prediction and interpretation of underlying characteristics of disease. This paper presents a novel algorithm that integrates latent class models with supervised learning. The new algorithm uses latent class models to cluster patients within groups that results in improved classification as well as aiding the understanding of the dissimilarities of the discovered groups. The methods are tested on data from patients with Systemic Sclerosis (SSc), a rare potentially fatal condition. Results show that the "Latent Class Multi-Label Classification Model" improves accuracy when compared with competitive similar methods.

Type: Proceedings paper
Title: Latent class multi-label classification to identify subclasses of disease for improved prediction
Event: The 32nd International Symposium on Computer-Based Medical Systems (CBMS)
Location: Cordoba, Spain
Dates: 5th-7th June 2019
ISBN-13: 978-1-7281-2286-1
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/CBMS.2019.00109
Publisher version: https://doi.org/10.1109/CBMS.2019.00109
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Prediction algorithms, Classification algorithms, Predictive models, Clustering algorithms, Diseases, Skin, Medical diagnostic imaging
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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/10081924
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