TY - JOUR
T1 - Detection of Alzheimer’s Disease Progression Using Integrated Deep Learning Approaches
AU - Shetty, Jayashree
AU - Shetty, Nisha P.
AU - Kothikar, Hrushikesh
AU - Mowla, Saleh
AU - Anand, Aiswarya
AU - Hegde, Veeraj
N1 - Publisher Copyright:
© 2023, Tech Science Press. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Alzheimer’s disease (AD) is an intensifying disorder that causes brain cells to degenerate early and destruct. Mild cognitive impairment (MCI) is one of the early signs of AD that interferes with people’s regular functioning and daily activities. The proposed work includes a deep learning approach with a multimodal recurrent neural network (RNN) to predict whether MCI leads to Alzheimer’s or not. The gated recurrent unit (GRU) RNN classifier is trained using individual and correlated features. Feature vectors are concatenated based on their correlation strength to improve prediction results. The feature vectors generated are given as the input to multiple different classifiers, whose decision function is used to predict the final output, which determines whether MCI progresses onto AD or not. Our findings demonstrated that, compared to individual modalities, which provided an average accuracy of 75%, our prediction model for MCI conversion to AD yielded an improvement in accuracy up to 96% when used with multiple concatenated modalities. Comparing the accuracy of different decision functions, such as Support Vector Machine (SVM), Decision tree, Random Forest, and Ensemble techniques, it was found that that the Ensemble approach provided the highest accuracy (96%) and Decision tree provided the lowest accuracy (86%).
AB - Alzheimer’s disease (AD) is an intensifying disorder that causes brain cells to degenerate early and destruct. Mild cognitive impairment (MCI) is one of the early signs of AD that interferes with people’s regular functioning and daily activities. The proposed work includes a deep learning approach with a multimodal recurrent neural network (RNN) to predict whether MCI leads to Alzheimer’s or not. The gated recurrent unit (GRU) RNN classifier is trained using individual and correlated features. Feature vectors are concatenated based on their correlation strength to improve prediction results. The feature vectors generated are given as the input to multiple different classifiers, whose decision function is used to predict the final output, which determines whether MCI progresses onto AD or not. Our findings demonstrated that, compared to individual modalities, which provided an average accuracy of 75%, our prediction model for MCI conversion to AD yielded an improvement in accuracy up to 96% when used with multiple concatenated modalities. Comparing the accuracy of different decision functions, such as Support Vector Machine (SVM), Decision tree, Random Forest, and Ensemble techniques, it was found that that the Ensemble approach provided the highest accuracy (96%) and Decision tree provided the lowest accuracy (86%).
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U2 - 10.32604/iasc.2023.039206
DO - 10.32604/iasc.2023.039206
M3 - Article
AN - SCOPUS:85165149354
SN - 1079-8587
VL - 37
SP - 1345
EP - 1362
JO - Intelligent Automation and Soft Computing
JF - Intelligent Automation and Soft Computing
IS - 2
ER -