Department of Computer Science, Faculty of Computers and Information, Damanhour University, 22511, Damanhour, Egypt.
Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, 41522, Ismailia, Egypt.
Artif Intell Med. 2023 Sep;143:102605. doi: 10.1016/j.artmed.2023.102605. Epub 2023 Jun 7.
Machine learning (ML) has demonstrated its ability to exploit important relationships within data collection, which can be used in the diagnosis, treatment, and prediction of outcomes in a variety of clinical contexts. Anxiety mental disorder analysis is one of the pending difficulties that ML can help with. A thorough study is demanded to gain a better understanding of this illness. Since the anxiety data is generally multidimensional, which complicates processing and as a result of technology improvements, medical data from several perspectives, known as multiview data (MVD), is being collected. Each view has its own data type and feature values, so there is a lot of diversity. This work introduces a novel preprocessing feature selection (FS) approach, multiview harris hawk optimization (MHHO), which has the potential to reduce the dimensionality of anxiety data, hence reducing analytical effort. The uniqueness of MHHO originates from combining a multiview linking methodology with the power of the harris hawk optimization (HHO) method. The HHO is used to identify the lowest optimal MVD feature subset, while multiview linking is utilized to find a promising fitness function to direct the HHO FS while accounting for all data views' heterogeneity. The complexity of MHHO is O(THL), where T is the number of iterations, H is the number of involved harris hawks, and L is the number of objects. Using two publicly available anxiety MVDs, MHHO is validated against ten recent rivals in its category. The experimental findings show that MHHO has a considerable advantage in terms of convergence speed (converging in less than ten iterations), subset size (removing 75% of the views; reducing feature size by 66%), and classification accuracy (approaching 100%). Furthermore, statistical analyses reveal that MHHO is statistically different from its competitors, bolstering its applicability. Finally, feature importance is evaluated, shedding light on the most anxiety-inducing characteristics. The likelihood of developing additional disorders (such as depression or stress) is also investigated.
机器学习（ML）已经证明了其在挖掘数据集中重要关系方面的能力，可用于各种临床环境下的诊断、治疗和结果预测。焦虑症的分析是 ML 可以帮助解决的待解决问题之一。需要进行深入研究以更好地了解这种疾病。由于焦虑数据通常是多维的，这使得处理变得复杂，并且随着技术的进步，从多个角度收集了称为多视图数据（MVD）的医学数据。每个视图都有自己的数据类型和特征值，因此存在很多差异。这项工作引入了一种新颖的预处理特征选择（FS）方法，即多视图哈里斯鹰优化（MHHO），它有可能降低焦虑数据的维度，从而减少分析工作量。MHHO 的独特之处在于结合了多视图链接方法和哈里斯鹰优化（HHO）方法的优势。HHO 用于识别最低的最优 MVD 特征子集，而多视图链接用于找到有前途的适应度函数，以指导 HHO FS，同时考虑到所有数据视图的异质性。MHHO 的复杂性为 O(THL)，其中 T 是迭代次数，H 是参与的哈里斯鹰的数量，L 是对象的数量。使用两个公开的焦虑 MVD，MHHO 与该类别中的十个最新竞争对手进行了比较。实验结果表明，MHHO 在收敛速度（不到十次迭代即可收敛）、子集大小（删除 75%的视图；将特征大小减少 66%）和分类精度（接近 100%）方面具有明显优势。此外，统计分析表明 MHHO 在统计学上与竞争对手不同，从而增强了它的适用性。最后，评估了特征重要性，揭示了最能引起焦虑的特征。还研究了发展其他疾病（如抑郁或压力）的可能性。
