New feature selection methods based on opposition-based learning and self-adaptive cohort intelligence for predicting patient no-shows

Abstract

Patient no-shows have significant adverse effects on healthcare systems. Therefore, predicting patients’ no-shows is necessary to use their appointment slots effectively. In the literature, filter feature selection methods have been prominently used for patient no-show prediction. However, filter methods are less effective than wrapper methods. This paper presents new wrapper methods based on three variants of the proposed algorithm, Opposition-based Self-Adaptive Cohort Intelligence (OSACI). The three variants of OSACI are referred to in this paper as OSACI-Init, OSACI-Update, and OSACI-Init_Update, which are formed by the integration of Self-Adaptive Cohort Intelligence (SACI) with three Opposition-based Learning (OBL) strategies; namely: OBL initialization, OBL update, and OBL initialization and update, respectively. The performance of the proposed algorithms was examined and compared with that of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and SACI in terms of AUC, sensitivity, specificity, dimensionality reduction, and convergence speed. Patient no-show data of a primary care clinic in upstate New York was used in the numerical experiments. The results showed that the proposed algorithms outperformed the other compared algorithms by achieving higher dimensionality reduction and better convergence speed while achieving comparable AUC, sensitivity, and specificity scores.