{"title":"环境辅助生活中基于深度智能的心力衰竭预测解决方案","authors":"Md. Ishan Arefin Hossain, Anika Tabassum, Zia Ush Shamszaman","doi":"10.1007/s43926-023-00043-4","DOIUrl":null,"url":null,"abstract":"Abstract Heart failure and heart disease prediction in real-time is a highly significant necessity for the patients living under the observation of Internet of Things-based Ambient Assisted Living systems because cardiovascular diseases are the most common fatal chronic diseases. Most of the solutions regarding heart disease prediction in the Internet of Things-based medical systems are relying on server-based predictive analysis which can appear to be complex for generating real-time prediction notifications and unreliable in case of any network interruption occurrences. The suggested edge-based solution for the prediction of heart disease from collected sensor data in real-time using a proposed lightweight deep learning technique called Oversampled Quinary Feed Forward Network (OQFFN) provides a less complex framework and more reliable notification system in case of network failure for the disease prediction which also reduces the need of forwarding all the data to the server resulting in reduced network bottleneck.","PeriodicalId":34751,"journal":{"name":"Discover Internet of Things","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep edge intelligence-based solution for heart failure prediction in ambient assisted living\",\"authors\":\"Md. Ishan Arefin Hossain, Anika Tabassum, Zia Ush Shamszaman\",\"doi\":\"10.1007/s43926-023-00043-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Heart failure and heart disease prediction in real-time is a highly significant necessity for the patients living under the observation of Internet of Things-based Ambient Assisted Living systems because cardiovascular diseases are the most common fatal chronic diseases. Most of the solutions regarding heart disease prediction in the Internet of Things-based medical systems are relying on server-based predictive analysis which can appear to be complex for generating real-time prediction notifications and unreliable in case of any network interruption occurrences. The suggested edge-based solution for the prediction of heart disease from collected sensor data in real-time using a proposed lightweight deep learning technique called Oversampled Quinary Feed Forward Network (OQFFN) provides a less complex framework and more reliable notification system in case of network failure for the disease prediction which also reduces the need of forwarding all the data to the server resulting in reduced network bottleneck.\",\"PeriodicalId\":34751,\"journal\":{\"name\":\"Discover Internet of Things\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Discover Internet of Things\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s43926-023-00043-4\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Discover Internet of Things","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s43926-023-00043-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep edge intelligence-based solution for heart failure prediction in ambient assisted living
Abstract Heart failure and heart disease prediction in real-time is a highly significant necessity for the patients living under the observation of Internet of Things-based Ambient Assisted Living systems because cardiovascular diseases are the most common fatal chronic diseases. Most of the solutions regarding heart disease prediction in the Internet of Things-based medical systems are relying on server-based predictive analysis which can appear to be complex for generating real-time prediction notifications and unreliable in case of any network interruption occurrences. The suggested edge-based solution for the prediction of heart disease from collected sensor data in real-time using a proposed lightweight deep learning technique called Oversampled Quinary Feed Forward Network (OQFFN) provides a less complex framework and more reliable notification system in case of network failure for the disease prediction which also reduces the need of forwarding all the data to the server resulting in reduced network bottleneck.
期刊介绍:
Discover Internet of Things is part of the Discover journal series committed to providing a streamlined submission process, rapid review and publication, and a high level of author service at every stage. It is an open access, community-focussed journal publishing research from across all fields relevant to the Internet of Things (IoT), providing cutting-edge and state-of-art research findings to researchers, academicians, students, and engineers.
Discover Internet of Things is a broad, open access journal publishing research from across all fields relevant to IoT. Discover Internet of Things covers concepts at the component, hardware, and system level as well as programming, operating systems, software, applications and other technology-oriented research topics. The journal is uniquely interdisciplinary because its scope spans several research communities, ranging from computer systems to communication, optimisation, big data analytics, and application. It is also intended that articles published in Discover Internet of Things may help to support and accelerate Sustainable Development Goal 9: ‘Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation’.
Discover Internet of Things welcomes all observational, experimental, theoretical, analytical, mathematical modelling, data-driven, and applied approaches that advance the study of all aspects of IoT research.