Voting Ensemble Approach for Enhancing Alzheimer's Disease Classification.
Subhajit ChatterjeeYung-Cheol ByunPublished in: Sensors (Basel, Switzerland) (2022)
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.
Keyphrases
- deep learning
- machine learning
- mild cognitive impairment
- end stage renal disease
- cognitive decline
- newly diagnosed
- ejection fraction
- cognitive impairment
- chronic kidney disease
- magnetic resonance imaging
- peritoneal dialysis
- healthcare
- convolutional neural network
- contrast enhanced
- bipolar disorder
- physical activity
- working memory
- oxidative stress
- air pollution
- artificial intelligence
- minimally invasive
- photodynamic therapy
- ionic liquid
- subarachnoid hemorrhage