Medical Data Mining published an article entitled Mapping the global research trends and hotspots on hypertensive nephropathy:A novel bibliometrics overview on 10 October 2025.The author confirmed this article’s proo...Medical Data Mining published an article entitled Mapping the global research trends and hotspots on hypertensive nephropathy:A novel bibliometrics overview on 10 October 2025.The author confirmed this article’s proof on 28 September 2025 without any questions.However,on 13 November 2025,the Editorial Office of Medical Data Mining noticed an inconsistency between the data presented in the main text and Figure 1.Specifically,erroneous Figure 1 states“a total of 56,691 literatures were obtained through database search”,while the main text in the Search results section states“According to the search term,a total of 59,220 publications were retrieved from the database.”The authors acknowledge that the original version of Figure 1 was incorrect and have provided the revised,correct version in this corrigendum.The authors would like to assert that there is no change in the body text of the article.展开更多
The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare I...The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.展开更多
Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy.In this background,several authentication and accessibility issues emerge ...Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy.In this background,several authentication and accessibility issues emerge with an inten-tion to protect the sensitive details of the patients over getting published in open domain.To solve this problem,Multi Attribute Case based Privacy Preservation(MACPP)technique is proposed in this study to enhance the security of privacy-preserving data.Private information can be any attribute information which is categorized as sensitive logs in a patient’s records.The semantic relation between transactional patient records and access rights is estimated based on the mean average value to distinguish sensitive and non-sensitive information.In addition to this,crypto hidden policy is also applied here to encrypt the sensitive data through symmetric standard key log verification that protects the personalized sensitive information.Further,linear integrity verification provides authentication rights to verify the data,improves the performance of privacy preserving techni-que against intruders and assures high security in healthcare setting.展开更多
文摘Medical Data Mining published an article entitled Mapping the global research trends and hotspots on hypertensive nephropathy:A novel bibliometrics overview on 10 October 2025.The author confirmed this article’s proof on 28 September 2025 without any questions.However,on 13 November 2025,the Editorial Office of Medical Data Mining noticed an inconsistency between the data presented in the main text and Figure 1.Specifically,erroneous Figure 1 states“a total of 56,691 literatures were obtained through database search”,while the main text in the Search results section states“According to the search term,a total of 59,220 publications were retrieved from the database.”The authors acknowledge that the original version of Figure 1 was incorrect and have provided the revised,correct version in this corrigendum.The authors would like to assert that there is no change in the body text of the article.
文摘The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.
文摘Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy.In this background,several authentication and accessibility issues emerge with an inten-tion to protect the sensitive details of the patients over getting published in open domain.To solve this problem,Multi Attribute Case based Privacy Preservation(MACPP)technique is proposed in this study to enhance the security of privacy-preserving data.Private information can be any attribute information which is categorized as sensitive logs in a patient’s records.The semantic relation between transactional patient records and access rights is estimated based on the mean average value to distinguish sensitive and non-sensitive information.In addition to this,crypto hidden policy is also applied here to encrypt the sensitive data through symmetric standard key log verification that protects the personalized sensitive information.Further,linear integrity verification provides authentication rights to verify the data,improves the performance of privacy preserving techni-que against intruders and assures high security in healthcare setting.
