医疗保健推系统的综合性框架:利用线性歧视性狼-卷积神经网络 (LDW-CNN) 模型
Vedna Sharma1, Surender Singh Samant1, Tej Singh2
1Department of Computer Science, Graphic Era (Deemed to be University), Dehradun 248002, India.
Diagnostics (Basel, Switzerland)
|November 27, 2024
概括
一个新的LDW-CNN模型通过解决阶级不平衡来改善医疗保健建议. 这种新的方法提高了疾病预测的准确性和诊断能力,以更好地照顾患者.
科学领域:
- 医疗保健中的人工智能
- 机器学习用于医学诊断
背景情况:
- 在医疗保健中,推系统对于预测患者和专业数据至关重要.
- 阶级不平衡是医疗保健推系统的一个重大挑战.
- 准确的预测和诊断需要高质量,可靠和认证的信息.
研究的目的:
- 解决医疗保健推系统中的阶级不平衡问题.
- 为了提高预测和诊断的准确性,使用新的LDW-CNN模型.
- 提高医疗保健推系统的性能.
主要方法:
- 线性判别狼 (LDW) 与卷积神经网络 (CNN) 的集成,形成了LDW-CNN模型.
- 使用灰狼优化器与线性差异分析进行增强预测.
- 对多种疾病数据集 (心脏,肝脏,脏) 的评估和与CNN和多级支持向量机器 (MSVM) 的比较.
主要成果:
- LDW-CNN模型实现了98.1%的准确性,超过了现有的深度学习方法.
- 与传统技术相比,提高了特异性 (99.18%) 和灵敏度 (99.008%).
- 在传统的CNN和MSVM方法中表现出优越的预测性能.
结论:
- LDW-CNN模型为多学科疾病预测和推提供了一个强大的解决方案.
- 它在医疗保健推系统中提供了卓越的性能.
- 该模型通过高准确性,特异性和灵敏性提高了多种疾病领域的预测和诊断.
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