Abstract
Support vector machine (SVM) using full features is a common approach for classifying diseases in healthcare systems. However, little literature reported to use it towards determining minimum features of biomarkers. This study introduced a bilevel mixed-integer optimization framework to detect minimum biomarker features for SVM. We proposed the two-population nested hybrid differential evolution (NHDE) to solve the problem. In case studies, two dominant biomarkers were found. The two-population NHDE algorithm was more efficient to achieve minimum biomarkers compared with one-population NHDE and traditional genetic algorithm.
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