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协同特征选择和分布式分类框架用于高维医学数据分析

D Dhinakaran¹, L Srinivasan², S Edwin Raja¹

¹Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.

|March 14, 2025

概括

一个新的算法,Synergistic Kruskal-RFE选择器和分布式多核分类框架 (SKR-DMKCF),通过显著减少特征和提高分类准确性来增强医疗数据分析. 这种方法为复杂的数据集提供了更好的效率和可扩展性.

科学领域:

医疗数据分析医学数据分析
机器学习机器学习
计算生物学计算生物学

背景情况:

医疗数据集庞大而复杂,导致计算挑战,内存限制和分类精度降低.
有效的特征选择和分类对于准确的医学数据解释和决策至关重要.

研究的目的:

引入一个集成算法,Synergistic Kruskal-RFE选择器和分布式多核分类框架 (SKR-DMKCF),以解决医疗数据分析的局限性.
为了提高复杂的医疗数据集中的维度减小,特征保存和分类性能.

主要方法:

开发了协同的Kruskal-RFE选择器和分布式多核分类框架 (SKR-DMKCF).
在分布式环境中利用递归特征消除和多核分类.
在四个不同的医疗数据集上评估了算法.

主要成果:

通过SKR-DMKCF实现了89%的平均特征减少比.
获得了平均分类准确率为85.3%,精度为81.5%,回忆率为84.7%.
与现有方法相比,证明了内存使用量减少了25%,并显著加快了速度.

结论:

在保留关键数据特征的同时,SKR-DMKCF有效地减少了维度.
拟议的框架提供了卓越的分类准确性和计算效率,确保了资源有限的环境的可扩展性.

关键词:

分布式计算分布式计算功能选择功能选择医疗数据分析医疗数据分析消除递归特征的消除.协同作用的Kruskal-RFE选择器和分布式多核分类框架 (SKR-DMKCF)和分类,以及分类和分类.

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