Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Diabetic Retinopathy01:27

Diabetic Retinopathy

65
DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...
65

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same journal

Deep Learning Based Framework for Detection and Classification of Leukemia Using Microscopic Images.

Microscopy research and technique·2026
Same journal

Externally Controlled In Situ SEM: Multi-Rate Scanning With Signal Regulation and Spatiotemporal Fusion.

Microscopy research and technique·2026
Same journal

In Situ TEM Observation of Phase Transformation Nucleation at the Near-Surface of Synthetic Aragonite.

Microscopy research and technique·2026
Same journal

Morpho-Anatomical and HPTLC Investigations of Lysimachia nummularia L. (Primulaceae) Grown in Switzerland.

Microscopy research and technique·2026
Same journal

Macroscopic, Histological and Ultrastructural Features of the Tongue of the Anatolian Wild Boar (Sus scrofa libycus).

Microscopy research and technique·2026
Same journal

Ultrastructural Insights Into the Reproductive Anatomy and Eggs of Cotton Pink Bollworm, Pectinophora gossypiella Saunders (Lepidoptera: Gelechiidae).

Microscopy research and technique·2026

相关实验视频

Updated: May 5, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K

双重多尺度注意力网络通过射箭鱼狩猎优化算法优化,用于糖尿病患者的预测预测.

Helina Rajini Suresh1, K Anita Davamani2,3, Hemalatha Chandrasekaran3

  • 1Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India.

Microscopy research and technique
|December 2, 2024
PubMed
概括

一个新的机器学习模型,DMSAN-AHO-DP,通过使用PIMA印度糖尿病数据集,提高了糖尿病预测的准确性. 与现有的糖尿病检测方法相比,这种先进的技术提供了更高的准确性和更低的错误率.

关键词:
射箭鱼的狩猎优化优化 射箭鱼的狩猎优化糖尿病人的预测预测双重多层次的关注网络.多层次的Haar波段具有融合网络的特点.

更多相关视频

The Three-Chamber Choice Behavioral Task using Zebrafish as a Model System
07:55

The Three-Chamber Choice Behavioral Task using Zebrafish as a Model System

Published on: April 14, 2021

3.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

368

相关实验视频

Last Updated: May 5, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
The Three-Chamber Choice Behavioral Task using Zebrafish as a Model System
07:55

The Three-Chamber Choice Behavioral Task using Zebrafish as a Model System

Published on: April 14, 2021

3.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

368

科学领域:

  • 医疗信息学 医疗信息学
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 糖尿病是一种影响血糖调节的慢性疾病.
  • 目前的糖尿病查依赖于多变量回归方法.
  • 数据收集和机器学习方面的进步为改进的预测模型提供了机会.

研究的目的:

  • 为准确的糖尿病预测提出了一种与弓箭鱼狩猎优化算法 (DMSAN-AHO-DP) 优化的新型双重多尺度注意力网络.
  • 提高数据质量和特征提取,以提高分类性能.
  • 评估拟议模型与现有糖尿病预测技术的有效性.

主要方法:

  • 使用PIMA印度糖尿病数据集 (PIDD) 进行模型培训和评估.
  • 应用对比度有限的自适应组图平衡过 (CLAHEF) 用于数据预处理和降噪.
  • 采用多级毛波特征融合网络 (MHWFFN) 进行特征提取.
  • 实施了双重多尺度注意网络 (DMSAN) 用于二元分类 (糖尿病/非糖尿病).
  • 使用弓箭鱼狩猎优化 (AHO) 算法优化了DMSAN超参数.
  • 在Python中开发了DMSAN-AHO-DP模型.

主要成果:

  • DMSAN-AHO-DP模型在糖尿病预测方面表现出卓越的表现.
  • 与EDNN-DP,ANN-DP和SVM-DNN-DP模型相比,在准确度方面取得了显著的改进 (23.52%,36.12%,31.12%更高).
  • 与基准模型相比,显示了较低的错误率 (16.05%,21.14%,31.02%较低).
  • 使用包括精度,F分数,灵敏度,特异性,精度,回忆和计算时间在内的指标评估性能.

结论:

  • 拟议的DMSAN-AHO-DP模型为糖尿病预测提供了一个高度准确和高效的方法.
  • 集成多层次的注意力机制和启发式优化显著提高预测能力.
  • 这种方法在利用机器学习用于早期糖尿病检测和管理方面提供了有希望的进步.