{"title":"利用物联网感知医疗监控系统提高生活水平","authors":"N. Mirza, Rawad Bader, Adnan Ali, M. Ishak","doi":"10.1109/FMEC57183.2022.10062818","DOIUrl":null,"url":null,"abstract":"Nowadays, even after many advancements in the field of healthcare facilities, one of the leading causes of death is considered to be heart disease. The numbers are soaring with every passing year due to the complexity involved in treating and diagnosing heart diseases. Most forms of heart disease can be prevented, but numbers are constantly increasing due to the inadequate number of preventive techniques. Various scholars have used machine learning and algorithm related to data mining to develop predictive systems for heart diseases. In this paper, a simple yet effective hybrid model is designed to predict early disease detection in a human being and deliver solutions for healthy living. It's unique compared to other proposed methods, as it combines the utilization of the Internet of Things, 5G, and artificial intelligence all at once. IoT and 5G facilitate real-time data collection related to the daily pattern of the subject, and AI is used for the predictive model. Results collected from the tested and trained data are accurate. Therefore, the proposed approach can be implemented to work as an alert system by publishing daily analyses and predictive reports together for early prevention.","PeriodicalId":129184,"journal":{"name":"2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Leveraging the Internet of Things Aware Healthcare Monitoring System for Better Living Standards\",\"authors\":\"N. Mirza, Rawad Bader, Adnan Ali, M. Ishak\",\"doi\":\"10.1109/FMEC57183.2022.10062818\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, even after many advancements in the field of healthcare facilities, one of the leading causes of death is considered to be heart disease. The numbers are soaring with every passing year due to the complexity involved in treating and diagnosing heart diseases. Most forms of heart disease can be prevented, but numbers are constantly increasing due to the inadequate number of preventive techniques. Various scholars have used machine learning and algorithm related to data mining to develop predictive systems for heart diseases. In this paper, a simple yet effective hybrid model is designed to predict early disease detection in a human being and deliver solutions for healthy living. It's unique compared to other proposed methods, as it combines the utilization of the Internet of Things, 5G, and artificial intelligence all at once. IoT and 5G facilitate real-time data collection related to the daily pattern of the subject, and AI is used for the predictive model. Results collected from the tested and trained data are accurate. Therefore, the proposed approach can be implemented to work as an alert system by publishing daily analyses and predictive reports together for early prevention.\",\"PeriodicalId\":129184,\"journal\":{\"name\":\"2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FMEC57183.2022.10062818\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FMEC57183.2022.10062818","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Leveraging the Internet of Things Aware Healthcare Monitoring System for Better Living Standards
Nowadays, even after many advancements in the field of healthcare facilities, one of the leading causes of death is considered to be heart disease. The numbers are soaring with every passing year due to the complexity involved in treating and diagnosing heart diseases. Most forms of heart disease can be prevented, but numbers are constantly increasing due to the inadequate number of preventive techniques. Various scholars have used machine learning and algorithm related to data mining to develop predictive systems for heart diseases. In this paper, a simple yet effective hybrid model is designed to predict early disease detection in a human being and deliver solutions for healthy living. It's unique compared to other proposed methods, as it combines the utilization of the Internet of Things, 5G, and artificial intelligence all at once. IoT and 5G facilitate real-time data collection related to the daily pattern of the subject, and AI is used for the predictive model. Results collected from the tested and trained data are accurate. Therefore, the proposed approach can be implemented to work as an alert system by publishing daily analyses and predictive reports together for early prevention.