electrocardiography; learning (artificial intelligence); medical diagnostic computing; medical disorders; neural nets; obstetrics; Artificial Neural Networks; automatic identification; birth; cardiotocogram diagnosis; categorisation; class inequality; computational intelligence; fetal cerebral palsy; healthy cases; machine learning algorithms; mortality; pathological cases; performance comparison; Accuracy; Classification algorithms; Educational institutions; Embryo; Fetal heart rate; Machine learning algorithms; Training; Artificial Neural Networks; Cardiotocograms; Machine Learning Algorithms; WEKA;
机译:利用旋转森林进行分类器集成,以提高机器学习算法的医学诊断性能。
机译:使用DCE-MRI对计算机辅助诊断乳腺癌计算机辅助诊断的分类器性能的比较
机译:实用IP流量分类的五种机器学习算法的初步性能比较
机译:机器学习算法诊断类不等心电图的性能比较
机译:基于机器学习分类算法的MMC-HVDC的内部故障诊断
机译:超声乳房病变中传统机器学习算法卷积神经网络和自动视力的性能评价:比较研究
机译:MRI图像脑肿瘤分类机器学习算法的性能分析与比较