A remote healthcare monitoring framework for diabetes prediction using machine learning
Abstract Diabetes is a metabolic disease that affects millions of people each year. It is associated with an increased likelihood of vital organ failures and decreased quality of life. Early detection and regular monitoring are crucial for managing diabetes. Remote patient monitoring can facilitate...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-06-01
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| Series: | Healthcare Technology Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/htl2.12010 |
| _version_ | 1797894180633051136 |
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| author | Jayroop Ramesh Raafat Aburukba Assim Sagahyroon |
| author_facet | Jayroop Ramesh Raafat Aburukba Assim Sagahyroon |
| author_sort | Jayroop Ramesh |
| collection | DOAJ |
| description | Abstract Diabetes is a metabolic disease that affects millions of people each year. It is associated with an increased likelihood of vital organ failures and decreased quality of life. Early detection and regular monitoring are crucial for managing diabetes. Remote patient monitoring can facilitate effective intervention and treatment paradigms using current technology. This work proposes an end‐to‐end remote monitoring framework for automated diabetes risk prediction and management, using personal health devices, smart wearables and smartphones. A support vector machine was developed for diabetes risk prediction using the Pima Indian Diabetes Database, after feature scaling, imputation, selection and augmentation. This work achieved the performance metrics of accuracy, sensitivity and specificity scores at 83.20%, 87.20% and 79% respectively through the tenfold stratified cross validation method, which is competitive with existing methods. Patients can use multiple healthcare devices, smartphones and smartwatches to measure vital parameters, curb the progression of diabetes and close the communication loop with medical professionals. The proposed framework enables medical professionals to make informed decisions based on the latest diabetes risk predictions and lifestyle insights while attaining unobtrusiveness, reduced cost, and vendor interoperability. |
| first_indexed | 2024-04-10T07:05:55Z |
| format | Article |
| id | doaj.art-341e61a90f874bf1813f4a923ebca399 |
| institution | Directory Open Access Journal |
| issn | 2053-3713 |
| language | English |
| last_indexed | 2024-04-10T07:05:55Z |
| publishDate | 2021-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Healthcare Technology Letters |
| spelling | doaj.art-341e61a90f874bf1813f4a923ebca3992023-02-27T08:31:42ZengWileyHealthcare Technology Letters2053-37132021-06-0183455710.1049/htl2.12010A remote healthcare monitoring framework for diabetes prediction using machine learningJayroop Ramesh0Raafat Aburukba1Assim Sagahyroon2Computer Science and Engineering American University of Sharjah Sharjah United Arab EmiratesComputer Science and Engineering American University of Sharjah Sharjah United Arab EmiratesComputer Science and Engineering American University of Sharjah Sharjah United Arab EmiratesAbstract Diabetes is a metabolic disease that affects millions of people each year. It is associated with an increased likelihood of vital organ failures and decreased quality of life. Early detection and regular monitoring are crucial for managing diabetes. Remote patient monitoring can facilitate effective intervention and treatment paradigms using current technology. This work proposes an end‐to‐end remote monitoring framework for automated diabetes risk prediction and management, using personal health devices, smart wearables and smartphones. A support vector machine was developed for diabetes risk prediction using the Pima Indian Diabetes Database, after feature scaling, imputation, selection and augmentation. This work achieved the performance metrics of accuracy, sensitivity and specificity scores at 83.20%, 87.20% and 79% respectively through the tenfold stratified cross validation method, which is competitive with existing methods. Patients can use multiple healthcare devices, smartphones and smartwatches to measure vital parameters, curb the progression of diabetes and close the communication loop with medical professionals. The proposed framework enables medical professionals to make informed decisions based on the latest diabetes risk predictions and lifestyle insights while attaining unobtrusiveness, reduced cost, and vendor interoperability.https://doi.org/10.1049/htl2.12010Optical, image and video signal processingBiomedical communicationComputer vision and image processing techniquesData handling techniquesMedical administrationPatient diagnostic methods and instrumentation |
| spellingShingle | Jayroop Ramesh Raafat Aburukba Assim Sagahyroon A remote healthcare monitoring framework for diabetes prediction using machine learning Healthcare Technology Letters Optical, image and video signal processing Biomedical communication Computer vision and image processing techniques Data handling techniques Medical administration Patient diagnostic methods and instrumentation |
| title | A remote healthcare monitoring framework for diabetes prediction using machine learning |
| title_full | A remote healthcare monitoring framework for diabetes prediction using machine learning |
| title_fullStr | A remote healthcare monitoring framework for diabetes prediction using machine learning |
| title_full_unstemmed | A remote healthcare monitoring framework for diabetes prediction using machine learning |
| title_short | A remote healthcare monitoring framework for diabetes prediction using machine learning |
| title_sort | remote healthcare monitoring framework for diabetes prediction using machine learning |
| topic | Optical, image and video signal processing Biomedical communication Computer vision and image processing techniques Data handling techniques Medical administration Patient diagnostic methods and instrumentation |
| url | https://doi.org/10.1049/htl2.12010 |
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