A Fuzzy time-series prediction model with multi-biological data for health management

• Published in: 2012 Joint 6th International Conference on Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS) pp. 1259 - 1264
• Main Authors: Tanii, H., Kuramoto, K., Nakajima, H., Kobashi, S., Tsuchiya, N., Hata, Y.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2012
• Subjects: autoregressive model; body weight; healthcare system; prediction model; time-series biological data
• ISBN: 9781467327428; 1467327425
• DOI: 10.1109/SCIS-ISIS.2012.6505205

This paper proposes a body weight prediction method using Fuzzy prediction model. Fuzzy prediction model is constructed by an autoregressive (AR) model based on body weight data and linear prediction models based on biological data. The biological data are obtained by pedometers such as number of steps, calorie consumption and so on. The Fuzzy prediction model is fixed by solving Yule-Walker equation and minimizing the Akaike's Information Criterion. In our experiment, the model predicts body weight change for next p days where p is the order of AR model. Then, four linear prediction models related to the biological data are constructed by linear regression analysis. We make a fuzzy membership function based on mean absolute error between body weight data and predicted value of each prediction model. Furthermore, these models are optimized for each subject in prediction models which add the biological data to AR model based on the mean absolute error. We employed 452 volunteers, and collected their body weight time-series data and the biological data during 730 days. We use these data from 1st to 365th day as learning data to determine the Fuzzy prediction model. As the result, the Fuzzy prediction model obtained higher correlation coefficient between predicted and truth values than the AR model on most subjects. In addition, the Fuzzy prediction model obtained smaller mean absolute prediction error than the AR model.