An Evolutionary Federated Learning Approach to Diagnose Alzheimer’s Disease Under Uncertainty
<b>Background:</b> Alzheimer’s disease (AD) leads to severe cognitive impairment and functional decline in patients, and its exact cause remains unknown. Early diagnosis of AD is imperative to enable timely interventions that can slow the progression of the disease. This research tackles...
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MDPI AG
2025-01-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/1/80 |
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| author | Nanziba Basnin Tanjim Mahmud Raihan Ul Islam Karl Andersson |
| author_facet | Nanziba Basnin Tanjim Mahmud Raihan Ul Islam Karl Andersson |
| author_sort | Nanziba Basnin |
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| description | <b>Background:</b> Alzheimer’s disease (AD) leads to severe cognitive impairment and functional decline in patients, and its exact cause remains unknown. Early diagnosis of AD is imperative to enable timely interventions that can slow the progression of the disease. This research tackles the complexity and uncertainty of AD by employing a multimodal approach that integrates medical imaging and demographic data. <b>Methods:</b> To scale this system to larger environments, such as hospital settings, and to ensure the sustainability, security, and privacy of sensitive data, this research employs both deep learning and federated learning frameworks. MRI images are pre-processed and fed into a convolutional neural network (CNN), which generates a prediction file. This prediction file is then combined with demographic data and distributed among clients for local training. Training is conducted both locally and globally using a belief rule base (BRB), which effectively integrates various data sources into a comprehensive diagnostic model. <b>Results:</b> The aggregated data values from local training are collected on a central server. Various aggregation methods are evaluated to assess the performance of the federated learning model, with results indicating that FedAvg outperforms other methods, achieving a global accuracy of 99.9%. <b>Conclusions:</b> The BRB effectively manages the uncertainty associated with AD data, providing a robust framework for integrating and analyzing diverse information. This research not only advances AD diagnostics by integrating multimodal data but also underscores the potential of federated learning for scalable, privacy-preserving healthcare solutions. |
| format | Article |
| id | doaj-art-85a8bf45e9e3430ab33eadfdbc5f0f95 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-85a8bf45e9e3430ab33eadfdbc5f0f952025-01-10T13:16:39ZengMDPI AGDiagnostics2075-44182025-01-011518010.3390/diagnostics15010080An Evolutionary Federated Learning Approach to Diagnose Alzheimer’s Disease Under UncertaintyNanziba Basnin0Tanjim Mahmud1Raihan Ul Islam2Karl Andersson3Cybersecurity Laboratory, Luleå University of Technology, 97187 Luleå, SwedenDepartment of Computer Science and Engineering, Rangamati Science and Technology University, Rangamati 4500, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka 1212, BangladeshCybersecurity Laboratory, Luleå University of Technology, 97187 Luleå, Sweden<b>Background:</b> Alzheimer’s disease (AD) leads to severe cognitive impairment and functional decline in patients, and its exact cause remains unknown. Early diagnosis of AD is imperative to enable timely interventions that can slow the progression of the disease. This research tackles the complexity and uncertainty of AD by employing a multimodal approach that integrates medical imaging and demographic data. <b>Methods:</b> To scale this system to larger environments, such as hospital settings, and to ensure the sustainability, security, and privacy of sensitive data, this research employs both deep learning and federated learning frameworks. MRI images are pre-processed and fed into a convolutional neural network (CNN), which generates a prediction file. This prediction file is then combined with demographic data and distributed among clients for local training. Training is conducted both locally and globally using a belief rule base (BRB), which effectively integrates various data sources into a comprehensive diagnostic model. <b>Results:</b> The aggregated data values from local training are collected on a central server. Various aggregation methods are evaluated to assess the performance of the federated learning model, with results indicating that FedAvg outperforms other methods, achieving a global accuracy of 99.9%. <b>Conclusions:</b> The BRB effectively manages the uncertainty associated with AD data, providing a robust framework for integrating and analyzing diverse information. This research not only advances AD diagnostics by integrating multimodal data but also underscores the potential of federated learning for scalable, privacy-preserving healthcare solutions.https://www.mdpi.com/2075-4418/15/1/80Alzheimer’s diseaseconvolutional neural network (CNN)federated learningbelief rule baseFedAvgFedProx |
| spellingShingle | Nanziba Basnin Tanjim Mahmud Raihan Ul Islam Karl Andersson An Evolutionary Federated Learning Approach to Diagnose Alzheimer’s Disease Under Uncertainty Diagnostics Alzheimer’s disease convolutional neural network (CNN) federated learning belief rule base FedAvg FedProx |
| title | An Evolutionary Federated Learning Approach to Diagnose Alzheimer’s Disease Under Uncertainty |
| title_full | An Evolutionary Federated Learning Approach to Diagnose Alzheimer’s Disease Under Uncertainty |
| title_fullStr | An Evolutionary Federated Learning Approach to Diagnose Alzheimer’s Disease Under Uncertainty |
| title_full_unstemmed | An Evolutionary Federated Learning Approach to Diagnose Alzheimer’s Disease Under Uncertainty |
| title_short | An Evolutionary Federated Learning Approach to Diagnose Alzheimer’s Disease Under Uncertainty |
| title_sort | evolutionary federated learning approach to diagnose alzheimer s disease under uncertainty |
| topic | Alzheimer’s disease convolutional neural network (CNN) federated learning belief rule base FedAvg FedProx |
| url | https://www.mdpi.com/2075-4418/15/1/80 |
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