A Generalized Weighted Aggregation Bernstein Operator Based on Fuzzy Medical Feature Analysis
Bernstein OperatorS
Abstract
This study presents a fuzzy approximation framework for binary medical diagnosis based on the Bernstein family of operators. Firsti, a linearized form of the Generalized Weighted Averaging (GWA) Bernstein operator is first introduced, and its main approximation properties are analyzed. Then, a normalized logarithmic extension, called the Log-GWA Bernstein operator, is applied to the UCI Breast Cancer Wisconsin (Diagnostic) dataset. Diagnostic features are modeled using Gaussian membership functions, and the resulting fuzzy approximations are aggregated to obtain patient-level risk scores. The findings indicate that the proposed approach provides a reliable and interpretable framework for medical decision-making.
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References
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