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SHR Neuro Cancer Cardio Lipid Metab Microb

Pfeifer, B; Kreuzthaler, M.
Calibrated kNN classification via second-layer neighborhood analysis
ADV DATA ANAL CLASSI. 2025; Doi: 10.1007/s11634-025-00654-5
Web of Science FullText FullText_MUG

Leading authors Med Uni Graz: Kreuzthaler Markus Eduard; Pfeifer Bastian
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Abstract:: The integration of artificial intelligence (AI) into medical practices has emphasized the importance of ensuring the reliability of predictive confidence, as it influences decision-making and the efficacy of AI-driven solutions. This paper focuses on the utilization of a non-parametric machine learning method, with a particular focus on the computation of confidence scores for individual classifications. Unlike parametric approaches, non-parametric techniques like k-nearest neighbors (kNN) offer flexibility without imposing strict data distribution assumptions. Leveraging this flexibility, we propose a novel kNN approach that introduces confidence-awareness through a two-layered neighborhood analysis. The developed approach is intended to support the classical non-parametric kNN classifier by providing more reliable and trustworthy class probabilities. Experimental evaluations conducted on benchmark datasets as well as a de-identified clinical real-world Electronic Health Records (EHR) data table consisting of thousands of unique class labels demonstrate the effectiveness of our approach in enhancing both, prediction accuracy and certainty assessment.
Find related publications in this database (Keywords): Machine learning; kNN; Calibration; Electronic Health Records