有效的特征选择与基于注意力的深猫卷积堆叠稀疏自编码器用于糖尿病预测
G Thilagavathi1, N K Karthikeyan1
1Department of Information Technology, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India.
Computer methods in biomechanics and biomedical engineering
|January 27, 2026
概括
早期发现糖尿病对于预防至关重要. 这项研究引入了使用改进的猎优化 (ICO) 和基于双重注意力的深猫卷积堆叠稀疏自编码器 (DA_DCC_SSAE) 的深度学习方法,用于准确的早期糖尿病识别.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 计算生物学 计算生物学
背景情况:
- 糖尿病是一种全球性健康问题,需要早期发现才能有效管理.
- 目前的诊断方法可以通过先进的计算技术来改进,以便更早地识别疾病.
研究的目的:
- 开发和验证一种用于早期发现糖尿病的新型深度学习模型.
- 增强特征选择,以提高糖尿病预测的诊断准确性.
主要方法:
- 使用改进的猎优化 (ICO) 算法进行数据预处理和功能选择.
- 使用基于双重注意力的深猫卷积堆叠稀疏自编码器 (DA_DCC_SSAE) 模型对糖尿病进行分类.
- 在多个数据集上评估模型的性能.
主要成果:
- 拟议的DA_DCC_SSAE模型在四个数据集中实现了高准确率:98.4% (数据集-1),98% (数据集-2),97.4% (数据集-3) 和96.8% (数据集-4).
- ICO特征选择方法有助于提高分类性能.
- 深度学习方法显示了早期糖尿病识别的巨大潜力.
结论:
- 新的深度学习框架,包括ICO和DA_DCC_SSAE,为早期糖尿病检测提供了一个有希望和高度准确的方法.
- 这种方法可以帮助及时进行干预,潜在地预防糖尿病并发症.
- 进一步的研究可以探索这种人工智能驱动的诊断工具的临床整合.
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