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Machine learning approach to flare-up detection and clustering in chronic obstructive pulmonary disease (COPD) patients

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Abstract

Purpose

Chronic obstructive pulmonary disease (COPD) is a prevalent and preventable condition that typically worsens over time. Acute exacerbations of COPD significantly impact disease progression, underscoring the importance of prevention efforts. This observational study aimed to achieve two main objectives: (1) identify patients at risk of exacerbations using an ensemble of clustering algorithms, and (2) classify patients into distinct clusters based on disease severity.

Methods

Data from portable medical devices were analyzed post-hoc using hyperparameter optimization with Self-Organizing Maps (SOM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest, and Support Vector Machine (SVM) algorithms, to detect flare-ups. Principal Component Analysis (PCA) followed by KMeans clustering was applied to categorize patients by severity.

Results

25 patients were included within the study population, data from 17 patients had the required reliability. Five patients were identified in the highest deterioration group, with one clinically confirmed exacerbation accurately detected by our ensemble algorithm. Then, PCA and KMeans clustering grouped patients into three clusters based on severity: Cluster 0 started with the least severe characteristics but experienced decline, Cluster 1 consistently showed the most severe characteristics, and Cluster 2 showed slight improvement.

Conclusion

Our approach effectively identified patients at risk of exacerbations and classified them by disease severity. Although promising, the approach would need to be verified on a larger sample with a larger number of recorded clinically verified exacerbations.

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Acknowledgements

Authors are especially indebted to all the participants that were open to try new approaches in the daily monitoring of their health status by personal devices.

Funding

The research leading to these results received funding from the Horizon 2020 (H2020) Framework Programme of the European Union for Research Innovation under Grant Agreement No 857159-SHAPES-H2020-SC1-FA-DTS-2018-2020.

Author information

Authors and Affiliations

  1. Tree Technology, Asturias, Spain

    Ramón Rueda, Esteban Fabello & Tatiana Silva

  2. Department of Pulmonary Diseases and Tuberculosis, University Hospital Olomouc, Zdravotníků 248/7, 77900, Olomuc, Czech Republic

    Samuel Genzor & Jan Mizera

  3. Czech National e-Health Center, University Hospital Olomouc, Olomuc, Czech Republic

    Ladislav Stanke

Authors
  1. Ramón Rueda
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Correspondence to Ramón Rueda.

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Rueda, R., Fabello, E., Silva, T. et al. Machine learning approach to flare-up detection and clustering in chronic obstructive pulmonary disease (COPD) patients. Health Inf Sci Syst 12, 50 (2024). https://doi.org/10.1007/s13755-024-00308-4

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