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New Feature Selection Methods Based On Opposition-Based Learning and Self-Adaptive Cohort Intelligence For Predicting Patient No-Shows by Ewen McPhee is a document available to read on EtoBox.

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This paper introduces new wrapper feature selection methods based on Opposition-based Self-Adaptive Cohort Intelligence (OSACI) for predicting patient no-shows, which significantly impact healthcare systems. The proposed methods, OSACI-Init, OSACI-Update, and OSACI-Init_Update, integrate Self-Adaptive Cohort Intelligence with various Opposition-based Learning strategies and demonstrate improved performance compared to existing algorithms like Genetic Algorithm and Particle Swarm Optimization. The study high

Author

Ewen McPhee

Language

EN