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

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 author

Designing a neuro-symbolic dual-model architecture for explainable and resilient intrusion detection in IoT networks.

Scientific reports·2025
Same author

Evaluating large transformer models for anomaly detection of resource-constrained IoT devices for intrusion detection system.

Scientific reports·2025
Same author

Transfer learning with XAI for robust malware and IoT network security.

Scientific reports·2025
Same author

A synergistic approach using digital twins and statistical machine learning for intelligent residential energy modelling.

Scientific reports·2025
Same author

Digital twin based deep learning framework for personalized thermal comfort prediction and energy efficient operation in smart buildings.

Scientific reports·2025
Same author

A proposed biometric authentication hybrid approach using iris recognition for improving cloud security.

Heliyon·2024

相关实验视频

Updated: Jun 11, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

使用机器学习算法和可解释的AI方法预测心脏病的拟议技术.

Hosam El-Sofany1, Belgacem Bouallegue2,3, Yasser M Abd El-Latif4

  • 1College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia. helsofany@kku.edu.sa.

Scientific reports
|October 7, 2024
PubMed
概括

这项研究开发了一种准确的机器学习模型,用于使用特征选择和各种算法预测早期心脏病. 该XGBoost模型实现了97.57%的准确性,使得快速和具有成本效益的检测.

关键词:
心脏病:心脏病是一种心脏病.机器学习算法ML算法机器学习是机器学习.这就是 SHAP SHAP 的意思.在SMOTE中使用.

更多相关视频

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

7.6K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.2K

相关实验视频

Last Updated: Jun 11, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
07:51

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

7.6K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.2K

科学领域:

  • 医疗数据分析 医疗数据分析
  • 机器学习在医疗保健中的应用.
  • 心血管疾病的研究研究.

背景情况:

  • 准确的心脏病预测对于早期干预和降低死亡率至关重要.
  • 目前的方法受到医疗保健专业人员无法提供患者持续监督的限制.
  • 机器学习 (ML) 提供了一种数据驱动的方法,以提高心脏病检测中的预测和决策.

研究的目的:

  • 开发一个准确的ML算法用于早期心脏病预测,使用多种特征选择策略.
  • 为了确定最有效的ML算法和功能子集来预测心脏病.
  • 创建一个基于症状的即时心脏病风险评估的移动应用程序.

主要方法:

  • 使用千平方,ANOVA和相互信息方法 (SF-1,SF-2,SF-3) 进行特征选择.
  • 在私人和公共数据集上评估十个ML算法,包括SVM,XGBoost和随机森林.
  • 应用合成少数超样本技术 (SMOTE) 进行数据平衡和SHAP用于可解释的人工智能.

主要成果:

  • 在组合数据集上使用SF-2特征子集的XGBoost算法实现了最佳性能.
  • 关键性能指标包括97.57%的精度,96.61%的灵敏度,90.48%的特异性,95.00%的精度,92.68%的F1得分和98%的AUC.
  • 使用SHAP开发了一种可解释的AI方法来解释模型预测.

结论:

  • 拟议的ML方法为医疗保健提供者提供了快速和成本效益的早期心脏病诊断.
  • 开发的移动应用程序提供基于用户输入的症状即时预测心脏病风险.
  • 这项研究强调了先进的ML技术,如XGBoost和SHAP在改善心血管诊断方面的潜力.