在使用HDBN和CAEN框架的医疗保健系统中优化疾病预测
G Prabaharan1, S M Udhaya Sankar2, V Anusuya3
1Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.
MethodsX
|May 20, 2025
概括
本研究引入了一种新的混合框架,用于改进分类和细分,提高医疗保健和物联网应用的准确性和减少错误. 新模型为复杂的数据集提供了强大的,可扩展的解决方案.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 分类和细分对于医疗保健,物联网和边缘计算至关重要,但在大型,多样化的数据集上,准确性和特异性面临挑战.
- 现有的方法在尽量减少假阳性和假阴性方面扎,限制了它们在现实世界中的适用性.
研究的目的:
- 开发一个强大的混合框架,以提高分类和细分的性能.
- 为了解决当前方法的准确性,精度和特异性的局限性.
主要方法:
- 这是一个混合框架,它结合了混合深信网络 (HDBN) 进行特征提取,以及自定义自适应组合网络 (CAEN) 进行动态预测聚合.
- 整合一个优化机制,以在各种数据集中实现适应性和稳定性.
主要成果:
- 在四个不同的数据集上实现了93%的准确性,87%的精度,95%的特异性和91%的回忆.
- 证明了高可靠性,马修斯相关系数为0.8932.
- 该框架为可扩展,高性能分类和细分建立了一个新的基准.
结论:
- 拟议的混合框架在分类和细分方面取得了重大进展,为现实应用提供了强大的解决方案.
- 该框架的适应性和性能为未来与可解释的人工智能和实时系统的整合铺平了道路.
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