Enhancing Chronic Disease Prediction in IoMT-Enabled Healthcare 5.0 Using Deep Machine Learning: Alzheimer’s Disease as a Case Study
Chronic disease significantly affects health on a global scale. Deep machine learning algorithms have found widespread application in the diagnosis of chronic diseases. Early diagnosis and treatment reduce the chance of a disease getting worse and, as a result, raise related mortality. The main obje...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10841945/ |
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| author | Rabia Javed Tahir Abbas Tariq Shahzad Khadija Kanwal Sadaqat Ali Ramay Muhammad Adnan Khan Khmaies Ouahada |
| author_facet | Rabia Javed Tahir Abbas Tariq Shahzad Khadija Kanwal Sadaqat Ali Ramay Muhammad Adnan Khan Khmaies Ouahada |
| author_sort | Rabia Javed |
| collection | DOAJ |
| description | Chronic disease significantly affects health on a global scale. Deep machine learning algorithms have found widespread application in the diagnosis of chronic diseases. Early diagnosis and treatment reduce the chance of a disease getting worse and, as a result, raise related mortality. The main objective of this work is to present a deep machine learning-based approach that provides better results in terms of accuracy. These findings have significance for tailored healthcare 5.0, enabling healthcare professionals to predict chronic disease more efficiently. A comparative examination of the most recent methods has been provided in our work reveals that it might be more advantageous to use the proposed model in which segmentation of the MRI is performed using U-net architecture and then classification is done using transfer learning for chronic disease prediction. Our proposed model provides 96.06% accuracy, it advances our understanding of deep machine learning’s potential for chronic disease prediction and emphasizes the need to tailor model selection to specific disease types using data from IoMT enabled devices. In order to make advanced improvement in the field of healthcare 5.0, future studies should focus on refining these models and investigating how well they work with a wider range of datasets. |
| format | Article |
| id | doaj-art-0b750172e2a54726a32cca0b997eafe8 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0b750172e2a54726a32cca0b997eafe82025-01-25T00:01:39ZengIEEEIEEE Access2169-35362025-01-0113142521427210.1109/ACCESS.2025.352551410841945Enhancing Chronic Disease Prediction in IoMT-Enabled Healthcare 5.0 Using Deep Machine Learning: Alzheimer’s Disease as a Case StudyRabia Javed0Tahir Abbas1Tariq Shahzad2https://orcid.org/0000-0001-5718-5585Khadija Kanwal3https://orcid.org/0009-0001-2300-9636Sadaqat Ali Ramay4Muhammad Adnan Khan5https://orcid.org/0000-0003-4854-9935Khmaies Ouahada6https://orcid.org/0000-0002-8462-5061Department of Computer Science, TIMES Institute, Multan, PakistanDepartment of Computer Science, TIMES Institute, Multan, PakistanDepartment of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South AfricaInstitute of CS and IT, The Women University, Multan, PakistanDepartment of Computer Science, TIMES Institute, Multan, PakistanDepartment of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, Republic of KoreaDepartment of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South AfricaChronic disease significantly affects health on a global scale. Deep machine learning algorithms have found widespread application in the diagnosis of chronic diseases. Early diagnosis and treatment reduce the chance of a disease getting worse and, as a result, raise related mortality. The main objective of this work is to present a deep machine learning-based approach that provides better results in terms of accuracy. These findings have significance for tailored healthcare 5.0, enabling healthcare professionals to predict chronic disease more efficiently. A comparative examination of the most recent methods has been provided in our work reveals that it might be more advantageous to use the proposed model in which segmentation of the MRI is performed using U-net architecture and then classification is done using transfer learning for chronic disease prediction. Our proposed model provides 96.06% accuracy, it advances our understanding of deep machine learning’s potential for chronic disease prediction and emphasizes the need to tailor model selection to specific disease types using data from IoMT enabled devices. In order to make advanced improvement in the field of healthcare 5.0, future studies should focus on refining these models and investigating how well they work with a wider range of datasets.https://ieeexplore.ieee.org/document/10841945/Chronic diseaseAlzheimer diseasedeep machine learningIoMTtransfer learningimage segmentation |
| spellingShingle | Rabia Javed Tahir Abbas Tariq Shahzad Khadija Kanwal Sadaqat Ali Ramay Muhammad Adnan Khan Khmaies Ouahada Enhancing Chronic Disease Prediction in IoMT-Enabled Healthcare 5.0 Using Deep Machine Learning: Alzheimer’s Disease as a Case Study IEEE Access Chronic disease Alzheimer disease deep machine learning IoMT transfer learning image segmentation |
| title | Enhancing Chronic Disease Prediction in IoMT-Enabled Healthcare 5.0 Using Deep Machine Learning: Alzheimer’s Disease as a Case Study |
| title_full | Enhancing Chronic Disease Prediction in IoMT-Enabled Healthcare 5.0 Using Deep Machine Learning: Alzheimer’s Disease as a Case Study |
| title_fullStr | Enhancing Chronic Disease Prediction in IoMT-Enabled Healthcare 5.0 Using Deep Machine Learning: Alzheimer’s Disease as a Case Study |
| title_full_unstemmed | Enhancing Chronic Disease Prediction in IoMT-Enabled Healthcare 5.0 Using Deep Machine Learning: Alzheimer’s Disease as a Case Study |
| title_short | Enhancing Chronic Disease Prediction in IoMT-Enabled Healthcare 5.0 Using Deep Machine Learning: Alzheimer’s Disease as a Case Study |
| title_sort | enhancing chronic disease prediction in iomt enabled healthcare 5 0 using deep machine learning alzheimer x2019 s disease as a case study |
| topic | Chronic disease Alzheimer disease deep machine learning IoMT transfer learning image segmentation |
| url | https://ieeexplore.ieee.org/document/10841945/ |
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