Stratifying risk of disease in haematuria patients using machine learning techniques to improve diagnostics.

Autor:	Drożdż A; Personal Health Data Science Group, Sano - Centre for Computational Personalised Medicine - International Research Foundation, Krakow, Poland., Duggan B; South Eastern Health and Social Care Trust, Ulster Hospital Dundonald, Belfast, United Kingdom., Ruddock MW; Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom., Reid CN; Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom., Kurth MJ; Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom., Watt J; Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom., Irvine A; Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom., Lamont J; Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom., Fitzgerald P; Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom., O'Rourke D; Belfast Health and Social Care Trust, Belfast City Hospital, Belfast, United Kingdom., Curry D; Belfast Health and Social Care Trust, Belfast City Hospital, Belfast, United Kingdom., Evans M; Belfast Health and Social Care Trust, Belfast City Hospital, Belfast, United Kingdom., Boyd R; Northern Ireland Clinical Trials Network, Belfast City Hospital, Belfast, United Kingdom., Sousa J; Personal Health Data Science Group, Sano - Centre for Computational Personalised Medicine - International Research Foundation, Krakow, Poland.; Centre for Public Health, Institute of Clinical Sciences, Queen's University, Belfast, United Kingdom.
Jazyk:	angličtina
Zdroj:	Frontiers in oncology [Front Oncol] 2024 May 08; Vol. 14, pp. 1401071. Date of Electronic Publication: 2024 May 08 (Print Publication: 2024).
DOI:	10.3389/fonc.2024.1401071
Abstrakt:	Background: Detailed and invasive clinical investigations are required to identify the causes of haematuria. Highly unbalanced patient population (predominantly male) and a wide range of potential causes make the ability to correctly classify patients and identify patient-specific biomarkers a major challenge. Studies have shown that it is possible to improve the diagnosis using multi-marker analysis, even in unbalanced datasets, by applying advanced analytical methods. Here, we applied several machine learning algorithms to classify patients from the haematuria patient cohort (HaBio) by analysing multiple biomarkers and to identify the most relevant ones. Materials and Methods: We applied several classification and feature selection methods (k-means clustering, decision trees, random forest with LIME explainer and CACTUS algorithm) to stratify patients into two groups: healthy (with no clear cause of haematuria) or sick (with an identified cause of haematuria e.g., bladder cancer, or infection). The classification performance of the models was compared. Biomarkers identified as important by the algorithms were also analysed in relation to their involvement in the pathological processes. Results: Results showed that a high unbalance in the datasets significantly affected the classification by random forest and decision trees, leading to the overestimation of the sick class and low model performance. CACTUS algorithm was more robust to the unbalance in the dataset. CACTUS obtained a balanced accuracy of 0.747 for both genders, 0.718 for females and 0.803 for males. The analysis showed that in the classification process for the whole dataset: microalbumin, male gender, and tPSA emerged as the most informative biomarkers. For males: age, microalbumin, tPSA, cystatin C, BTA, HAD and S100A4 were the most significant biomarkers while for females microalbumin, IL-8, pERK, and CXCL16. Conclusions: CACTUS algorithm demonstrated improved performance compared with other methods such as decision trees and random forest. Additionally, we identified the most relevant biomarkers for the specific patient group, which could be considered in the future as novel biomarkers for diagnosis. Our results have the potential to inform future research and provide new personalised diagnostic approaches tailored directly to the needs of the individuals. Competing Interests: Authors MR, CR, MK, JW, AI, and JL were employed by Randox Laboratories Ltd but hold no shares in the company. PF is the Managing Director and owner of Randox Laboratories Ltd, a privately-owned company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Drożdż, Duggan, Ruddock, Reid, Kurth, Watt, Irvine, Lamont, Fitzgerald, O’Rourke, Curry, Evans, Boyd and Sousa.)
Databáze:	MEDLINE
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