A fuzzy logic approach to predict human body weight based on AR model

This paper proposes a body weight prediction method using auto regressive (AR) model and Fuzzy-AR model. First, we employ 6 persons body weight change data of 365 days. AR model predicts body weight of a day from these time-series data. We calculate an order of AR model for each person by Akaike...

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Bibliographic Details
Published in2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011) pp. 1022 - 1025
Main Authors Tanii, Hideaki, Nakajima, Hiroshi, Tsuchiya, Naoki, Kuramoto, Kei, Kobashi, Syoji, Hata, Yutaka
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2011
Subjects
autoregressive model
body weight
Brain modeling
Correlation
Data models
healthcare system
Mathematical model
prediction model
Predictive models
time-series data
Weight measurement
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ISBN9781424473151
1424473152
ISSN1098-7584
DOI10.1109/FUZZY.2011.6007361

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Summary:This paper proposes a body weight prediction method using auto regressive (AR) model and Fuzzy-AR model. First, we employ 6 persons body weight change data of 365 days. AR model predicts body weight of a day from these time-series data. We calculate an order of AR model for each person by Akaike's Information Criterion. In the experiment, we predicted body weight change of next day for those subjects. The AR model obtained 0.798 in correlation coefficient between predicted and truth values. Second, we propose a Fuzzy-AR model that predicts body weight of next p days from last p days, where p is the order of AR model. In this method, we propose a Fuzzy-AR model with the fuzzy membership function using last p days data. In the experiment, the Fuzzy-AR model obtained 0.558 in correlation coefficient on 2 subjects.
ISBN:9781424473151
1424473152
ISSN:1098-7584
DOI:10.1109/FUZZY.2011.6007361