DocumentCode
3117344
Title
A fuzzy logic approach to predict human body weight based on AR model
Author
Tanii, Hideaki ; Nakajima, Hiroshi ; Tsuchiya, Naoki ; Kuramoto, Kei ; Kobashi, Syoji ; Hata, Yutaka
Author_Institution
Grad. Sch. of Eng., Univ. of Hyogo, Kobe, Japan
fYear
2011
fDate
27-30 June 2011
Firstpage
1022
Lastpage
1025
Abstract
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.
Keywords
autoregressive processes; correlation methods; fuzzy logic; health care; prediction theory; time series; auto regressive model; body weight prediction method; correlation coefficient; fuzzy logic approach; fuzzy membership function; fuzzy-AR model; human body weight; information criterion; predicted body weight change; predicted values; time-series data; truth values; Brain modeling; Correlation; Data models; Mathematical model; Predictive models; Weight measurement; autoregressive model; body weight; healthcare system; prediction model; time-series data;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems (FUZZ), 2011 IEEE International Conference on
Conference_Location
Taipei
ISSN
1098-7584
Print_ISBN
978-1-4244-7315-1
Electronic_ISBN
1098-7584
Type
conf
DOI
10.1109/FUZZY.2011.6007361
Filename
6007361
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