A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ihnaini, Baha | - |
dc.contributor.author | Khan, M. A. | - |
dc.contributor.author | Khan, Tahir Abbas | - |
dc.contributor.author | Abbas, Sagheer | - |
dc.contributor.author | Daoud, Mohammad Sh | - |
dc.contributor.author | Ahmad, Munir | - |
dc.contributor.author | Khan, Muhammad Adnan | - |
dc.date.accessioned | 2021-10-15T01:40:33Z | - |
dc.date.available | 2021-10-15T01:40:33Z | - |
dc.date.created | 2021-10-15 | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 1687-5265 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82381 | - |
dc.description.abstract | The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system's performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system's improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | HINDAWI LTD | - |
dc.relation.isPartOf | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE | - |
dc.title | A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000700351700002 | - |
dc.identifier.doi | 10.1155/2021/4243700 | - |
dc.identifier.bibliographicCitation | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, v.2021 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85116295228 | - |
dc.citation.title | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE | - |
dc.citation.volume | 2021 | - |
dc.contributor.affiliatedAuthor | Khan, Muhammad Adnan | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Neurosciences | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.