Balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease: a pilot study
Abstract Machine learning algorithms that integrate multiple biomarkers are increasingly used in disease detection, yet economic considerations are often overlooked. Medial vascular calcification (mVC), a pathology associated with elevated cardiovascular risk in chronic kidney disease (CKD), require...
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Nature Portfolio
2025-05-01
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| Online Access: | https://doi.org/10.1038/s41598-025-02457-2 |
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| author | Urszula Bialonczyk Malgorzata Debowska Lu Dai Abdul Rashid Qureshi Leon Bobrowski Magnus Soderberg Bengt Lindholm Peter Stenvinkel Tomasz Lukaszuk Jan Poleszczuk |
| author_facet | Urszula Bialonczyk Malgorzata Debowska Lu Dai Abdul Rashid Qureshi Leon Bobrowski Magnus Soderberg Bengt Lindholm Peter Stenvinkel Tomasz Lukaszuk Jan Poleszczuk |
| author_sort | Urszula Bialonczyk |
| collection | DOAJ |
| description | Abstract Machine learning algorithms that integrate multiple biomarkers are increasingly used in disease detection, yet economic considerations are often overlooked. Medial vascular calcification (mVC), a pathology associated with elevated cardiovascular risk in chronic kidney disease (CKD), requires cost-effective diagnostic approaches. This pilot study evaluated the cost-effectiveness of machine learning models for mVC detection using traditional risk markers and circulating biomarkers in 152 CKD patients undergoing living donor kidney transplantation. Patients were classified as having no/minimal (n = 93) or moderate/extensive (n = 59) mVC. Five classification frameworks with automatic variable selection identified predictors of mVC. Age and copeptin were selected by all algorithms, while diabetes, male sex, choline, and osteoprotegerin were chosen by four methods. The number of features selected ranged from 5 to 21. Although accuracy differences among classifiers were limited to 3%, models using more features nearly tripled the procedure’s cost. By incorporating the incremental cost-effectiveness ratio, the study highlighted significant disparities in performance versus cost among classifiers. The present findings suggest that machine learning has the potential to complement imaging techniques for mVC detection and uncover novel biomarkers. However, modest performance improvements may not justify higher costs, underscoring the importance of considering cost-effectiveness when selecting classification models. |
| format | Article |
| id | doaj-art-74809b6118b747c3b0b746028fa5b80d |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-74809b6118b747c3b0b746028fa5b80d2025-08-20T01:53:22ZengNature PortfolioScientific Reports2045-23222025-05-0115111010.1038/s41598-025-02457-2Balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease: a pilot studyUrszula Bialonczyk0Malgorzata Debowska1Lu Dai2Abdul Rashid Qureshi3Leon Bobrowski4Magnus Soderberg5Bengt Lindholm6Peter Stenvinkel7Tomasz Lukaszuk8Jan Poleszczuk9Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of SciencesNalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of SciencesAging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm UniversityRenal Medicine and Baxter Novum, Department of Clinical Science, Intervention and Technology, Karolinska InstitutetNalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of SciencesPathology, Clinical Pharmacology and Safety Sciences, AstraZeneca R&DRenal Medicine and Baxter Novum, Department of Clinical Science, Intervention and Technology, Karolinska InstitutetRenal Medicine and Baxter Novum, Department of Clinical Science, Intervention and Technology, Karolinska InstitutetFaculty of Computer Science, Bialystok University of TechnologyNalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of SciencesAbstract Machine learning algorithms that integrate multiple biomarkers are increasingly used in disease detection, yet economic considerations are often overlooked. Medial vascular calcification (mVC), a pathology associated with elevated cardiovascular risk in chronic kidney disease (CKD), requires cost-effective diagnostic approaches. This pilot study evaluated the cost-effectiveness of machine learning models for mVC detection using traditional risk markers and circulating biomarkers in 152 CKD patients undergoing living donor kidney transplantation. Patients were classified as having no/minimal (n = 93) or moderate/extensive (n = 59) mVC. Five classification frameworks with automatic variable selection identified predictors of mVC. Age and copeptin were selected by all algorithms, while diabetes, male sex, choline, and osteoprotegerin were chosen by four methods. The number of features selected ranged from 5 to 21. Although accuracy differences among classifiers were limited to 3%, models using more features nearly tripled the procedure’s cost. By incorporating the incremental cost-effectiveness ratio, the study highlighted significant disparities in performance versus cost among classifiers. The present findings suggest that machine learning has the potential to complement imaging techniques for mVC detection and uncover novel biomarkers. However, modest performance improvements may not justify higher costs, underscoring the importance of considering cost-effectiveness when selecting classification models.https://doi.org/10.1038/s41598-025-02457-2ClassificationFeature selectionMedial vascular calcificationChronic kidney diseaseIncremental cost-effectiveness ratio |
| spellingShingle | Urszula Bialonczyk Malgorzata Debowska Lu Dai Abdul Rashid Qureshi Leon Bobrowski Magnus Soderberg Bengt Lindholm Peter Stenvinkel Tomasz Lukaszuk Jan Poleszczuk Balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease: a pilot study Scientific Reports Classification Feature selection Medial vascular calcification Chronic kidney disease Incremental cost-effectiveness ratio |
| title | Balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease: a pilot study |
| title_full | Balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease: a pilot study |
| title_fullStr | Balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease: a pilot study |
| title_full_unstemmed | Balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease: a pilot study |
| title_short | Balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease: a pilot study |
| title_sort | balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease a pilot study |
| topic | Classification Feature selection Medial vascular calcification Chronic kidney disease Incremental cost-effectiveness ratio |
| url | https://doi.org/10.1038/s41598-025-02457-2 |
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