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A STUDY ON THE PARALLELIZATION OF MOEAS TO PREDICT THE PATIENT'S RESPONSE TO THE ONABOTULINUMTOXINA TREATMENT

机译:摩克预测患者对OnababoTulinumtoxina治疗的反应的平行化研究

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This work deals with the decrease of the computational cost in the task of feature weighting for the predictive 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 predictive 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.
机译:这项工作涉及对具有Onaboleumtoxina(Bont-A)治疗偏头痛的响应的预测模型的特征权重的计算成本减少。更具体地,我们考虑支持并行化的多目标进化算法(Moeas)。所有这一切,目的是改善对治疗的响应的预测模型的培训时间。得到的结果表明,在使用8个螺纹时训练时间从8到少于2小时的训练时间获得接近84%的准确度。总而言之,与模拟退火相比,这项工作显着降低了特征加权执行时间,同时获得了类似的准确性值。

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