An application of LSTM prediction model based on keystroke data

Based on the subject's keyboard typing time series dataset, an long short term (LSTM) network model was developed to predict the early-stage Parkinson's disease. The training and test results show that the area under ROC curve (AUC) is 0.82, accuracy rate (ACC) is 0.84, precision (PRE) is 0.85, recall rate (REC) is 0.98, and F1 score is 0.90. This indicates that the LSTM prediction model can botain high accuracy, precision and sensitivity results by automatically extracting keyboard typing time series characteristics of keyboard typing time series data.