ORCID
- Eleni Rozaki: 0000-0002-1229-4013
Abstract
Clinical data mining and healthcare analytics enable systematic evaluation of treatment strategies in precision oncology. This study analysed a harmonised multi-centre dataset of 150 patients treated with linac-based stereotactic radiosurgery for vestibular schwannoma across three institutions in Ireland and the UK. A data-driven framework combining descriptive analytics, unsupervised clustering (K-means and Gaussian Mixture Models), and Random Forest modelling was used to assess treatment plan consistency, explore dose–response patterns, and estimate clinical outcomes. Clustering identified four treatment plan groups with distinct profiles of tumour reduction and organ-at-risk exposure. Random Forest models linked these clusters and dosimetric factors with tumour control and functional preservation. While internal performance was high, results are interpreted cautiously due to the limited sample size and absence of external validation. By integrating unsupervised learning with interpretable predictive modelling, this study provides a reproducible approach to characterising dose–response heterogeneity across centres. The findings support the future development of decision-support tools, while recognising that prescriptive optimisation requires further causal or optimisation-based modelling beyond the present work.
Keywords
Clinical analytics, Tumour modelling, Clinical Data mining, Healthcare optimisation, Predictive modelling, Treatment planning, Data mining
DOI Link
Publication Date
2026-01-01
Publication Title
Healthcare Analytics
Volume
9
Issue
100450
Deposit Date
2026-06-19
Funding
This research was funded by Technological University Dublin, with no specific grant number applicable.Open access publication was supported by the Irish Research eLibrary (IReL) through the Elsevier ScienceDirect transformative agreement for participating Irish institutions (ELS2023IE). Open access publication was supported by the Irish Research eLibrary (IReL) through the Elsevier ScienceDirect transformative agreement for participating Irish institutions (ELS2023IE).
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Rozaki, Eleni; Skourou, Christina; Fitzpatrick, David; Amoo, Michael; Minnock, Eoin; Hayhurst, Caroline; Palaniappan, Nachi; and Javadpour, Mohsen, "An unsupervised learning approach to optimising tumour therapy through clinical data mining" (2026). Research Outputs: 2025-Present. 6.
https://arrow.tudublin.ie/bushmarro/6