焦虑障碍分析的特征选择框架,使用一种新的多视图哈里斯·霍克优化算法
Ahmed Hamed1, Marwa F Mohamed2
1Department of Computer Science, Faculty of Computers and Information, Damanhour University, 22511, Damanhour, Egypt.
Artificial intelligence in medicine
|September 6, 2023
概括
机器学习 (ML) 通过一种新的多视图方法有效地分析复杂的焦虑数据. 这种方法显著减少了数据尺寸,并提高了心理健康应用的诊断准确性.
科学领域:
- 计算精神病学是一种计算精神病学.
- 机器学习在医疗保健中的应用
- 数据科学用于心理健康的数据科学.
背景情况:
- 机器学习 (ML) 为分析复杂的临床数据提供了强大的工具,有助于诊断,治疗和预测结果.
- 焦虑障碍的分析是一个重大挑战,因为患者数据的多维性和异质性而复杂化.
- 在医疗保健中,越来越多的多视图数据 (MVD) 需要先进的方法来进行有效的处理和分析.
研究的目的:
- 为焦虑数据引入一种新的预处理特征选择 (FS) 方法,称为多视图哈里斯霍克优化 (MHHO).
- 为了减少焦虑MVD的维度,从而减少分析复杂性和提高处理效率.
- 通过有效处理数据异质性,提高ML模型在分析焦虑障碍方面的准确性和可靠性.
主要方法:
- 通过将多视图链接方法与哈里斯霍克优化 (HHO) 算法集成,开发了MHHO.
- 利用多视图链接来创建一个适应性函数,该函数解释了不同数据视图中的异质性.
- 雇佣HHO从MVD中识别出最佳的低维特征子集,由开发的健身功能指导.
主要成果:
- MHHO展示了快速的融合,通常在十次代内,显著超过了最近的十个竞争对手的方法.
- 该方法实现了大量的数据减少,删除了75%的视图,并将功能大小减少了66%.
- 实现了接近100%的分类准确性,统计分析证实了其在竞争对手上的显著优势.
结论:
- MHHO是一种高度有效和高效的特征选择方法,用于焦虑MVD.
- 拟议的方法显著提高了分类准确性,并减少了精神障碍分析中的计算负载.
- 进一步的研究可以探索已识别的诱发焦虑的特征及其与抑郁症和压力等并发性疾病的相关性.
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