Balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease: a pilot study.

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.