Mood Prediction in Consideration of Certainty Factor Using Multilayer Deep Neural Network and Storage-Type Prediction Models
Sensors and Materials · 2016
Depression has become a social problem in Japan.To prevent depression, people need to recognize their mental health in daily life.Previous research supports mental health care by predicting tomorrow's mood with 73% accuracy using weather information and biological information.However, the mood after 2 d or later could not be predicted using an existing system.In this paper, we propose multilayer-deep neural network (M-DNN) and storage-type prediction models (STPMs) to predict mood two weeks in advance with high accuracy.The M-DNN outputs predictions as well as unpredictable data using a deep neural network and threshold optimization in each prediction layer.The threshold optimization determines the threshold that maximizes a certainty factor.The certainty factor is calculated from the predictive accuracy of M-DNN and the amount of unpredictable data.The STPMs interpolates the unpredictable data by accumulating the predictions output from M-DNN.The amount of unpredictable data output from the M-DNN is decreased by STPMs.Experiments show that M-DNN and STPMs can predict mood two weeks in advance with 70% accuracy.The predictive accuracy in M-DNN+STPMs is 11% higher than that in DNN.Hence, M-DNN+STPMs is an effective method for mood prediction.