• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

一种用于检测产前心脏病的图像处理方法。

An Image Processing Approach for Detection of Prenatal Heart Disease.

机构信息

Faculty of Engineering & Computer Technology, AIMST University, Bedong, Kedah 08100, Malaysia.

Department of Computer Science Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu 600124, India.

出版信息

Biomed Res Int. 2022 Aug 2;2022:2003184. doi: 10.1155/2022/2003184. eCollection 2022.

DOI:10.1155/2022/2003184
PMID:35958813
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9363204/
Abstract

Prenatal heart disease, generally known as cardiac problems (CHDs), is a group of ailments that damage the heartbeat and has recently now become top deaths worldwide. It connects a plethora of cardiovascular diseases risks to the urgent in need of accurate, trustworthy, and effective approaches for early recognition. Data preprocessing is a common method for evaluating big quantities of information in the medical business. To help clinicians forecast heart problems, investigators utilize a range of data mining algorithms to examine enormous volumes of intricate medical information. The system is predicated on classification models such as NB, KNN, DT, and RF algorithms, so it includes a variety of cardiac disease-related variables. It takes do with an entire dataset from the medical research database of patients with heart disease. The set has 300 instances and 75 attributes. Considering their relevance in establishing the usefulness of alternate approaches, only 15 of the 75 criteria are examined. The purpose of this research is to predict whether or not a person will develop cardiovascular disease. According to the statistics, naïve Bayes classifier has the highest overall accuracy.

摘要

先天性心脏病，通常被称为心脏疾病（CHD），是一组损害心跳的疾病，最近已成为全球头号死因。它将多种心血管疾病的风险与对准确、可信和有效的早期识别方法的迫切需求联系起来。数据预处理是评估医疗业务中大量信息的常用方法。为了帮助临床医生预测心脏问题，研究人员利用多种数据挖掘算法来检查大量复杂的医疗信息。该系统基于分类模型，如 NB、KNN、DT 和 RF 算法，因此它包含了多种与心脏疾病相关的变量。它涉及到心脏病患者医疗研究数据库中的整个数据集。该数据集共有 300 个实例和 75 个属性。考虑到它们在建立替代方法的有效性方面的相关性，仅检查了 75 个标准中的 15 个。本研究的目的是预测一个人是否会患上心血管疾病。根据统计数据，朴素贝叶斯分类器的整体准确率最高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/76cfafa739a3/BMRI2022-2003184.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/0794023cfb8b/BMRI2022-2003184.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/57735debdd21/BMRI2022-2003184.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/d15deaa49027/BMRI2022-2003184.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/8745e67b4237/BMRI2022-2003184.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/6c120273f703/BMRI2022-2003184.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/38882a923d95/BMRI2022-2003184.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/76cfafa739a3/BMRI2022-2003184.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/0794023cfb8b/BMRI2022-2003184.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/57735debdd21/BMRI2022-2003184.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/d15deaa49027/BMRI2022-2003184.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/8745e67b4237/BMRI2022-2003184.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/6c120273f703/BMRI2022-2003184.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/38882a923d95/BMRI2022-2003184.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c217/9363204/76cfafa739a3/BMRI2022-2003184.007.jpg

相似文献

1
An Image Processing Approach for Detection of Prenatal Heart Disease.一种用于检测产前心脏病的图像处理方法。
Biomed Res Int. 2022 Aug 2;2022:2003184. doi: 10.1155/2022/2003184. eCollection 2022.
2
The analysis of the effects of acute rheumatic fever in childhood on cardiac disease with data mining.运用数据挖掘分析儿童急性风湿热对心脏病的影响。
Int J Med Inform. 2019 Mar;123:68-75. doi: 10.1016/j.ijmedinf.2018.12.009. Epub 2018 Dec 27.
3
AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease.基于 K 折交叉验证的 AdaBoost 集成方法在心脏病早期检测中的生存能力研究。
Comput Intell Neurosci. 2022 Apr 18;2022:9005278. doi: 10.1155/2022/9005278. eCollection 2022.
4
Classification of Heart Disease Using MFO Based Neural Network on MRI Images.基于 MFO 神经网络的 MRI 图像下心电疾病分类。
Curr Med Imaging. 2021;17(9):1114-1127. doi: 10.2174/1573405617666210126153920.
5
Ensemble classifier fostered detection of arrhythmia using ECG data.基于 ECG 数据的集成分类器对心律失常进行检测。
Med Biol Eng Comput. 2023 Sep;61(9):2453-2466. doi: 10.1007/s11517-023-02839-6. Epub 2023 May 5.
6
Computer-assisted lip diagnosis on Traditional Chinese Medicine using multi-class support vector machines.基于多类支持向量机的中医唇诊计算机辅助诊断。
BMC Complement Altern Med. 2012 Aug 16;12:127. doi: 10.1186/1472-6882-12-127.
7
Lessons learned from data mining of WHO mortality database.从世界卫生组织死亡率数据库的数据挖掘中吸取的经验教训。
Methods Inf Med. 2011;50(4):380-5. doi: 10.3414/ME10-02-0019. Epub 2011 Jun 21.
8
Cardiac disease prediction using AI algorithms with SelectKBest.使用 AI 算法和 SelectKBest 进行心脏疾病预测。
Med Biol Eng Comput. 2023 Dec;61(12):3397-3408. doi: 10.1007/s11517-023-02918-8. Epub 2023 Sep 8.
9
Retinal Image Analysis for Ocular Disease Prediction Using Rule Mining Algorithms.基于规则挖掘算法的眼部疾病预测的视网膜图像分析。
Interdiscip Sci. 2021 Sep;13(3):451-462. doi: 10.1007/s12539-020-00373-9. Epub 2020 Jun 8.
10
Discovery of Knowledge in the Incidence of a Type of Lung Cancer for Patients through Data Mining Models.通过数据挖掘模型发现患者某类肺癌的发病知识。
Comput Intell Neurosci. 2022 May 31;2022:6058213. doi: 10.1155/2022/6058213. eCollection 2022.

引用本文的文献

1
Retracted: An Image Processing Approach for Detection of Prenatal Heart Disease.撤回：一种用于检测产前心脏病的图像处理方法。
Biomed Res Int. 2023 Dec 29;2023:9760542. doi: 10.1155/2023/9760542. eCollection 2023.

本文引用的文献

1
A Novel Deep Learning-Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology.一种基于深度学习的新型黑霉菌病识别方法，采用改进的混合学习方法。
Contrast Media Mol Imaging. 2022 Jan 27;2022:4352730. doi: 10.1155/2022/4352730. eCollection 2022.
2
Energetic Glaucoma Segmentation and Classification Strategies Using Depth Optimized Machine Learning Strategies.基于深度优化机器学习策略的青光眼能量分割与分类策略
Contrast Media Mol Imaging. 2021 Nov 25;2021:5709257. doi: 10.1155/2021/5709257. eCollection 2021.
3
Heart Disease Prediction System Using Decision Tree and Naive Bayes Algorithm.
基于决策树和朴素贝叶斯算法的心脏病预测系统
Curr Med Imaging Rev. 2019;15(8):712-717. doi: 10.2174/1573405614666180322141259.
4
Artificial plant optimization algorithm to detect heart rate & presence of heart disease using machine learning.基于机器学习的人工植物优化算法，用于检测心率和心脏病。
Artif Intell Med. 2020 Jan;102:101752. doi: 10.1016/j.artmed.2019.101752. Epub 2019 Nov 8.
5
Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias.个性化心律失常监测与预警系统。
Sci Rep. 2017 Aug 24;7(1):9270. doi: 10.1038/s41598-017-09544-z.
6
A random forest classifier for lymph diseases.用于淋巴疾病的随机森林分类器。
Comput Methods Programs Biomed. 2014 Feb;113(2):465-73. doi: 10.1016/j.cmpb.2013.11.004. Epub 2013 Nov 14.
7
Computer-aided diagnosis system: a Bayesian hybrid classification method.计算机辅助诊断系统：贝叶斯混合分类方法。
Comput Methods Programs Biomed. 2013 Oct;112(1):104-13. doi: 10.1016/j.cmpb.2013.05.029. Epub 2013 Aug 7.
8
Regional diastolic function in ischaemic heart disease using pulsed wave Doppler tissue imaging.使用脉冲波多普勒组织成像评估缺血性心脏病的局部舒张功能。
Eur Heart J. 1999 Apr;20(7):496-505. doi: 10.1053/euhj.1998.1278.