Parkinson’s Disease Detection using CNN-LSTM Model for Time-series Keystroke Data
• Proposed a CNN-LSTM model that outperforms baseline models, including SqueezeNet, MobileNet, and AlexNet, in predicting Parkinson’s disease.
• Proposed a solution for imbalanced data by performing time-series subsequence undersampling, achieving better performance compared to SMOTE.