Dementia, a progressive neurological disorder, is a leading cause of disability and death globally, often underdiagnosed in its early stages. Early diagnosis, prevention, and treatment are crucial for mitigating its impact on individuals and society.
Objective
This study aimed to predict the exact conversion time from normal cognition (NC) to mild cognitive impairment (MCI), and to provide insights for early diagnosis and treatment of dementia.
Methods
A novel dual attention convolutional network model was proposed to handle high-dimensional features and limited patients’ records in short-sequence time series data. It integrated feature and temporal attention modules to capture dependencies and used a custom loss function to enhance clinical interpretability.
Results
The model significantly reduced mean squared error (MSE) by 9.67% and mean absolute error (MAE) by 26.24%, while increasing the r-square () by 16.71% compared to a basic convolutional model. It effectively predicted NC to MCI conversion, offering valuable guidance for early intervention.
Conclusions
The dual attention convolutional network model effectively predicted NC to MCI conversion, providing a valuable tool for early dementia diagnosis and treatment.
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