This study proposes an objective method for mental health evaluation using a CNN-LSTM model trained on visual metrics time-series data. Data were collected from 16 cancer patients and 9 controls using TobiiPro eyeglasses while viewing artwork. Mental health metrics (hope, anxiety, well-being) were assessed using questionnaires. The model achieved high classification accuracy (93.81%, 94.76%, and 95.00% for hope, anxiety, and well-being, respectively) and could be integrated into home-based mental health monitoring applications.
Publisher
Translational Psychiatry
Published On
Authors
Somayeh B. Shafiei, Zaeem Lone, Ahmed S. Elsayed, Ahmed A. Hussein, Khurshid A. Guru
Tags
mental health
CNN-LSTM model
visual metrics
time-series data
classification accuracy
home-based monitoring
artwork
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