Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification
Alzheimer’s disease is dementia that impairs one’s thinking, behavior, and memory. It starts as a moderate condition affecting areas of the brain that make it challenging to retain recently learned information, causes mood swings, and causes confusion regarding occasions, times, and locations. The m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/19/7661 |
| _version_ | 1797476833755660288 |
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| author | Subhajit Chatterjee Yung-Cheol Byun |
| author_facet | Subhajit Chatterjee Yung-Cheol Byun |
| author_sort | Subhajit Chatterjee |
| collection | DOAJ |
| description | Alzheimer’s disease is dementia that impairs one’s thinking, behavior, and memory. It starts as a moderate condition affecting areas of the brain that make it challenging to retain recently learned information, causes mood swings, and causes confusion regarding occasions, times, and locations. The most prevalent type of dementia, called Alzheimer’s disease (AD), causes memory-related problems in patients. A precise medical diagnosis that correctly classifies AD patients results in better treatment. Currently, the most commonly used classification techniques extract features from longitudinal MRI data before creating a single classifier that performs classification. However, it is difficult to train a reliable classifier to achieve acceptable classification performance due to limited sample size and noise in longitudinal MRI data. Instead of creating a single classifier, we propose an ensemble voting method that generates multiple individual classifier predictions and then combines them to develop a more accurate and reliable classifier. The ensemble voting classifier model performs better in the Open Access Series of Imaging Studies (OASIS) dataset for older adults than existing methods in important assessment criteria such as accuracy, sensitivity, specificity, and AUC. For the binary classification of with dementia and no dementia, an accuracy of 96.4% and an AUC of 97.2% is attained. |
| first_indexed | 2024-03-09T21:09:22Z |
| format | Article |
| id | doaj.art-9a8a372fc637428eb0dc35c00b2e8a33 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:09:22Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9a8a372fc637428eb0dc35c00b2e8a332023-11-23T21:52:44ZengMDPI AGSensors1424-82202022-10-012219766110.3390/s22197661Voting Ensemble Approach for Enhancing Alzheimer’s Disease ClassificationSubhajit Chatterjee0Yung-Cheol Byun1Department of Computer Engineering, Jeju National University, Jeju 63243, KoreaDepartment of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, KoreaAlzheimer’s disease is dementia that impairs one’s thinking, behavior, and memory. It starts as a moderate condition affecting areas of the brain that make it challenging to retain recently learned information, causes mood swings, and causes confusion regarding occasions, times, and locations. The most prevalent type of dementia, called Alzheimer’s disease (AD), causes memory-related problems in patients. A precise medical diagnosis that correctly classifies AD patients results in better treatment. Currently, the most commonly used classification techniques extract features from longitudinal MRI data before creating a single classifier that performs classification. However, it is difficult to train a reliable classifier to achieve acceptable classification performance due to limited sample size and noise in longitudinal MRI data. Instead of creating a single classifier, we propose an ensemble voting method that generates multiple individual classifier predictions and then combines them to develop a more accurate and reliable classifier. The ensemble voting classifier model performs better in the Open Access Series of Imaging Studies (OASIS) dataset for older adults than existing methods in important assessment criteria such as accuracy, sensitivity, specificity, and AUC. For the binary classification of with dementia and no dementia, an accuracy of 96.4% and an AUC of 97.2% is attained.https://www.mdpi.com/1424-8220/22/19/7661Alzheimer’s diseasedeep learningclassificationensemble learningMRI data |
| spellingShingle | Subhajit Chatterjee Yung-Cheol Byun Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification Sensors Alzheimer’s disease deep learning classification ensemble learning MRI data |
| title | Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification |
| title_full | Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification |
| title_fullStr | Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification |
| title_full_unstemmed | Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification |
| title_short | Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification |
| title_sort | voting ensemble approach for enhancing alzheimer s disease classification |
| topic | Alzheimer’s disease deep learning classification ensemble learning MRI data |
| url | https://www.mdpi.com/1424-8220/22/19/7661 |
| work_keys_str_mv | AT subhajitchatterjee votingensembleapproachforenhancingalzheimersdiseaseclassification AT yungcheolbyun votingensembleapproachforenhancingalzheimersdiseaseclassification |