A Clustering model for gadgets and apps used in patient monitoring in HIOT environment
Background: with increasing demand for treatment, patients are monitored with help of Internet of Things(IOT). Patientchr('39')s monitoring devices and technologies include heart rate measurement, blood pressure measurement, blood glucose and other vital signs. The purpose of study is to p...
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| Format: | Article |
| Language: | fas |
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Tehran University of Medical Sciences
2019-10-01
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| Series: | بیمارستان |
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| Online Access: | http://jhosp.tums.ac.ir/article-1-5894-en.html |
| _version_ | 1818731515354284032 |
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| author | Mohssen Ghanavatinejad Mahdieh Tavakoli MohamadMehdi Sepehri |
| author_facet | Mohssen Ghanavatinejad Mahdieh Tavakoli MohamadMehdi Sepehri |
| author_sort | Mohssen Ghanavatinejad |
| collection | DOAJ |
| description | Background: with increasing demand for treatment, patients are monitored with help of Internet of Things(IOT). Patientchr('39')s monitoring devices and technologies include heart rate measurement, blood pressure measurement, blood glucose and other vital signs. The purpose of study is to provide a model of clustering patient physical monitoring gadgets and apps in Healthcare Internet of Things (HIOT) environment using data mining techniques, so based on the needs and characteristics of the user, the more appropriate results of choosing technologies acquired.
Materials and methods: This study is a review and functional since its result. The data includes 6 unique features of 60 selected technologies including function, price, connectivity route, power supply, location and type of use that has been extracted from R&D and advertising sites of technologies and also relevant articles. data analysis method is clustering technique and K-medoids algorithm. to identify the most effective features, random forest algorithm has been used.
Results: the proposed clustering model takes into account 6 as inputs and clusters gadgets and apps in accordance with selected characteristics as the model outputs. clustering problem data is clustered in 4 categories. Silhouette index is 0.45, which indicates the validity of the model. The type of application and then the price had the greatest impact on clustering.
Conclusion: By this model, patients or users can find the most appropriate technology based on the type of disease and other effective features, such as price. So with accurate physical and momentary monitoring, disease progression decrease and prevention of disease will improve. |
| first_indexed | 2024-12-17T23:18:54Z |
| format | Article |
| id | doaj.art-a09197b1eae74ababadf7cfd31726e4c |
| institution | Directory Open Access Journal |
| issn | 2008-1928 2228-7450 |
| language | fas |
| last_indexed | 2024-12-17T23:18:54Z |
| publishDate | 2019-10-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | بیمارستان |
| spelling | doaj.art-a09197b1eae74ababadf7cfd31726e4c2022-12-21T21:28:57ZfasTehran University of Medical Sciencesبیمارستان2008-19282228-74502019-10-011836372A Clustering model for gadgets and apps used in patient monitoring in HIOT environmentMohssen Ghanavatinejad0Mahdieh Tavakoli1MohamadMehdi Sepehri2 Master Industrial Engineering, Healthcare Systems, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran Master Industrial Engineering, Healthcare Systems, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran Professor, Healthcare System Engineering Group, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran (*Corresponding Author), Email: mehdi.sepehri@gmail.com Background: with increasing demand for treatment, patients are monitored with help of Internet of Things(IOT). Patientchr('39')s monitoring devices and technologies include heart rate measurement, blood pressure measurement, blood glucose and other vital signs. The purpose of study is to provide a model of clustering patient physical monitoring gadgets and apps in Healthcare Internet of Things (HIOT) environment using data mining techniques, so based on the needs and characteristics of the user, the more appropriate results of choosing technologies acquired. Materials and methods: This study is a review and functional since its result. The data includes 6 unique features of 60 selected technologies including function, price, connectivity route, power supply, location and type of use that has been extracted from R&D and advertising sites of technologies and also relevant articles. data analysis method is clustering technique and K-medoids algorithm. to identify the most effective features, random forest algorithm has been used. Results: the proposed clustering model takes into account 6 as inputs and clusters gadgets and apps in accordance with selected characteristics as the model outputs. clustering problem data is clustered in 4 categories. Silhouette index is 0.45, which indicates the validity of the model. The type of application and then the price had the greatest impact on clustering. Conclusion: By this model, patients or users can find the most appropriate technology based on the type of disease and other effective features, such as price. So with accurate physical and momentary monitoring, disease progression decrease and prevention of disease will improve.http://jhosp.tums.ac.ir/article-1-5894-en.htmlpatient monitoringgadget and apphealthcare internet of things (hiot)clustering |
| spellingShingle | Mohssen Ghanavatinejad Mahdieh Tavakoli MohamadMehdi Sepehri A Clustering model for gadgets and apps used in patient monitoring in HIOT environment بیمارستان patient monitoring gadget and app healthcare internet of things (hiot) clustering |
| title | A Clustering model for gadgets and apps used in patient monitoring in HIOT environment |
| title_full | A Clustering model for gadgets and apps used in patient monitoring in HIOT environment |
| title_fullStr | A Clustering model for gadgets and apps used in patient monitoring in HIOT environment |
| title_full_unstemmed | A Clustering model for gadgets and apps used in patient monitoring in HIOT environment |
| title_short | A Clustering model for gadgets and apps used in patient monitoring in HIOT environment |
| title_sort | clustering model for gadgets and apps used in patient monitoring in hiot environment |
| topic | patient monitoring gadget and app healthcare internet of things (hiot) clustering |
| url | http://jhosp.tums.ac.ir/article-1-5894-en.html |
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