Abstract A novel classification framework for clinical decision making that uses an Extremely Randomized Tree (ERT) based feature selection and a Diverse Intensified Strawberry Optimized Neural network (DISON) is proposed. DISON is a Feed Forward Artificial Neural Network where the optimization of weights and bias is done using a two phase training strategy. Two algorithms namely Strawberry Plant Optimization (SPO) algorithm and Gradient-descent Back-propagation algorithm are used sequentially to identify the optimum weights and bias. The novel two phase training method and the stochastic duplicate-elimination strategy of SPO helps in addressing the issue of local optima associated with conventional neural networks. The relevant attributes are selected based on the feature importance values computed using an ERT classifier. Vertebral Column, Pima Indian diabetes (PID), Cleveland Heart disease (CHD) and Statlog Heart disease (SHD) datasets from the University of California Irvine machine learning repository are used for experimentation. The framework has achieved an accuracy of 87.17% for Vertebral Column, 90.92% for PID, 93.67% for CHD and 94.5% for SHD. The classifier performance has been compared with existing works and is found to be competitive in terms of accuracy, sensitivity and specificity. Wilcoxon test confirms the statistical superiority of the proposed method.

中文翻译：

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使用多样化强化草莓优化神经网络 (DISON) 进行临床决策的分类框架

摘要 提出了一种新的临床决策分类框架，该框架使用基于极端随机树 (ERT) 的特征选择和多样化的强化草莓优化神经网络 (DISON)。DISON 是一种前馈人工神经网络，其中权重和偏差的优化是使用两阶段训练策略完成的。两种算法，即草莓植物优化（SPO）算法和梯度下降反向传播算法依次用于确定最佳权重和偏差。新颖的两阶段训练方法和 SPO 的随机重复消除策略有助于解决与传统神经网络相关的局部最优问题。基于使用 ERT 分类器计算的特征重要性值选择相关属性。脊柱，来自加利福尼亚大学欧文分校机器学习存储库的皮马印第安人糖尿病 (PID)、克利夫兰心脏病 (CHD) 和 Statlog 心脏病 (SHD) 数据集用于实验。该框架实现了 87.17% 的 Vertebral Column、90.92% 的 PID、93.67% 的 CHD 和 94.5% 的 SHD。分类器性能已与现有作品进行比较，发现在准确性、灵敏度和特异性方面具有竞争力。Wilcoxon 检验证实了所提出方法的统计优越性。分类器性能已与现有作品进行比较，发现在准确性、灵敏度和特异性方面具有竞争力。Wilcoxon 检验证实了所提出方法的统计优越性。分类器性能已与现有作品进行比较，发现在准确性、灵敏度和特异性方面具有竞争力。Wilcoxon 检验证实了所提出方法的统计优越性。