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A study on the parallelization of moeas to pbkp_redict the patient’s response to the onabotulinumtoxina treatment

Abstract:

This work deals with the decrease of the computational cost in the task of feature weighting for the pbkp_redictive models of the response to the treatment of migraine with OnabotulinumtoxinA (BoNT-A). More specifically, we consider the multiobjective evolutionary algorithms (MOEAs) that support parallelization. All this with the aim of improving the training times of pbkp_redictive models of response to the treatment. The results obtained show that accuracies close to 84 % are obtained while training times are decreased from 8 to less than 2 hours when using 8 threads. All in all, this work remarkably reduces the feature weighting execution time in comparison with Simulated Annealing, while getting similar values of accuracy.