A big data scheme for heart disease classification in map reduce using jellyfish search flow regime optimization enabled Spinalnet.

IF 1.7 4区医学 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS

Pace-Pacing and Clinical Electrophysiology Pub Date : 2024-07-01 Epub Date: 2024-05-15 DOI:10.1111/pace.14975

Antony Jaya Mabel Rani, Chinnapillai Srivenkateswaran, Gurunathan Vishnupriya, Nalini Subramanian, Poonguzhali Ilango, Vijaya Kumar Jacintha

{"title":"A big data scheme for heart disease classification in map reduce using jellyfish search flow regime optimization enabled Spinalnet.","authors":"Antony Jaya Mabel Rani, Chinnapillai Srivenkateswaran, Gurunathan Vishnupriya, Nalini Subramanian, Poonguzhali Ilango, Vijaya Kumar Jacintha","doi":"10.1111/pace.14975","DOIUrl":null,"url":null,"abstract":"Background: The disease related to the heart is serious and can lead to death. Precise heart disease prediction is imperative for the effective treatment of cardiac patients. This can be attained by machine learning (ML) techniques using healthcare data. Several models on the basis of ML predict and identify disease in the heart, but this model cannot manage a huge database because of the deficiency of the smart model. This paper provides an optimized SpinalNet with a MapReduce model to categorize heart disease.Objective: The objective is to design a big data approach for heart disease classification using the proposed Jellyfish Search Flow Regime Optimization (JSFRO)-based SpinalNet.Method: The binary image conversion is applied on Electrocardiogram (ECG) images for converting the image to binary image. MapReduce model is adapted, in which the mappers execute feature extraction and the reducer performs heart disease classification. In the mapper phase, the features like statistical features, shape features and temporal features are extracted and in reducer, the SpinalNet with JSFRO is considered. Here, the training of SpinalNet is done with JSFRO, which is produced by the unification of Jellyfish Search Optimization (JSO) and Flow Regime Optimization (FRO).Method: The JSFRO-based SpinalNet offered effectual performance with the finest accuracy of 90.8%, sensitivity of 95.2% and specificity of 93.6%.","PeriodicalId":54653,"journal":{"name":"Pace-Pacing and Clinical Electrophysiology","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pace-Pacing and Clinical Electrophysiology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1111/pace.14975","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/15 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

Background: The disease related to the heart is serious and can lead to death. Precise heart disease prediction is imperative for the effective treatment of cardiac patients. This can be attained by machine learning (ML) techniques using healthcare data. Several models on the basis of ML predict and identify disease in the heart, but this model cannot manage a huge database because of the deficiency of the smart model. This paper provides an optimized SpinalNet with a MapReduce model to categorize heart disease.

Objective: The objective is to design a big data approach for heart disease classification using the proposed Jellyfish Search Flow Regime Optimization (JSFRO)-based SpinalNet.

Method: The binary image conversion is applied on Electrocardiogram (ECG) images for converting the image to binary image. MapReduce model is adapted, in which the mappers execute feature extraction and the reducer performs heart disease classification. In the mapper phase, the features like statistical features, shape features and temporal features are extracted and in reducer, the SpinalNet with JSFRO is considered. Here, the training of SpinalNet is done with JSFRO, which is produced by the unification of Jellyfish Search Optimization (JSO) and Flow Regime Optimization (FRO).

Method: The JSFRO-based SpinalNet offered effectual performance with the finest accuracy of 90.8%, sensitivity of 95.2% and specificity of 93.6%.

查看原文本刊更多论文

利用水母搜索流机制优化的 Map Reduce 中心脏病分类的大数据方案启用了 Spinalnet。

背景介绍与心脏有关的疾病非常严重，可导致死亡。要想有效治疗心脏病患者，就必须进行精确的心脏病预测。这可以通过使用医疗数据的机器学习（ML）技术来实现。一些基于 ML 的模型可以预测和识别心脏疾病，但由于智能模型的不足，这种模型无法管理庞大的数据库。本文通过 MapReduce 模型提供了一个优化的 SpinalNet，用于对心脏病进行分类：目的是设计一种大数据方法，利用提出的基于水母搜索流机制优化（JSFRO）的 SpinalNet 进行心脏病分类：方法：对心电图（ECG）图像进行二值图像转换，将图像转换为二值图像。采用 MapReduce 模型，其中映射器执行特征提取，还原器执行心脏病分类。在映射器阶段，提取统计特征、形状特征和时间特征等特征，在还原器阶段，考虑使用 JSFRO 的 SpinalNet。这里，SpinalNet 的训练是通过 JSFRO 完成的，JSFRO 由水母搜索优化（JSO）和流式优化（FRO）统一而成：方法：基于 JSFRO 的 SpinalNet 具有良好的性能，最佳准确率为 90.8%，灵敏度为 95.2%，特异性为 93.6%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Pace-Pacing and Clinical Electrophysiology 医学-工程：生物医学

CiteScore

2.70

自引率

5.60%

发文量

209

审稿时长

2-4 weeks

期刊介绍： Pacing and Clinical Electrophysiology (PACE) is the foremost peer-reviewed journal in the field of pacing and implantable cardioversion defibrillation, publishing over 50% of all English language articles in its field, featuring original, review, and didactic papers, and case reports related to daily practice. Articles also include editorials, book reviews, Musings on humane topics relevant to medical practice, electrophysiology (EP) rounds, device rounds, and information concerning the quality of devices used in the practice of the specialty.