Extracting time-oriented relationships of nutrients to losing body fat mass using inductive logic programming

This study was performed to extract rules for reducing body fat mass so as to prevent lifestyle-related diseases. Lifestyle-related diseases have been increasing in Japan, even among younger people. Body fat mass is related to lifestyle-related diseases. Hence, finding rules for reducing body fat ma...

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Vydáno v:2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCICC) s. 226 - 230
Hlavní autoři: Ushikubo, Sho, Kanamori, Katsutoshi, Ohwada, Hayato
Médium: Konferenční příspěvek
Jazyk:angličtina
japonština
Vydáno: IEEE 01.08.2016
Témata:
Body fat mass
Calcium
Diseases
Fats
Feature extraction
Gaussian distribution
Inductive Logic Programming
Logic programming
Nutrients
Statistical analysis
Time-series data
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Abstract This study was performed to extract rules for reducing body fat mass so as to prevent lifestyle-related diseases. Lifestyle-related diseases have been increasing in Japan, even among younger people. Body fat mass is related to lifestyle-related diseases. Hence, finding rules for reducing body fat mass is very meaningful. We obtained lifestyle time-series data on five male subjects who are in their 20s and not obese. The data includes the amount of body fat mass of each subject and a variety of features such as sleep, exercise, and nutrient intake. We used Inductive Logic Programming (ILP) to apply this data because ILP can more flexibly learn rules than other machine-learning methods. As a result of applying the data to ILP, our ILP system successfully extracted rules of time-oriented relationships of nutrients to decrease body fat mass based on limited data. Intake of various nutrients one day and two days prior was effective in reducing body fat mass. Moreover, we determined that nutrients related to losing body fat mass include vitamin B2, pantothenic acid, fat, vitamin B1, and biotin.
AbstractList This study was performed to extract rules for reducing body fat mass so as to prevent lifestyle-related diseases. Lifestyle-related diseases have been increasing in Japan, even among younger people. Body fat mass is related to lifestyle-related diseases. Hence, finding rules for reducing body fat mass is very meaningful. We obtained lifestyle time-series data on five male subjects who are in their 20s and not obese. The data includes the amount of body fat mass of each subject and a variety of features such as sleep, exercise, and nutrient intake. We used Inductive Logic Programming (ILP) to apply this data because ILP can more flexibly learn rules than other machine-learning methods. As a result of applying the data to ILP, our ILP system successfully extracted rules of time-oriented relationships of nutrients to decrease body fat mass based on limited data. Intake of various nutrients one day and two days prior was effective in reducing body fat mass. Moreover, we determined that nutrients related to losing body fat mass include vitamin B2, pantothenic acid, fat, vitamin B1, and biotin.
Author Ohwada, Hayato
Ushikubo, Sho
Kanamori, Katsutoshi
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  organization: Fac. of Sci. & Tech., Tokyo Univ. of Sci., Noda, Japan
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  givenname: Katsutoshi
  surname: Kanamori
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  email: katsu@rs.tus.ac.jp
  organization: Fac. of Sci. & Tech., Tokyo Univ. of Sci., Noda, Japan
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  givenname: Hayato
  surname: Ohwada
  fullname: Ohwada, Hayato
  email: ohwada@rs.tus.ac.jp
  organization: Fac. of Sci. & Tech., Tokyo Univ. of Sci., Noda, Japan
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Snippet This study was performed to extract rules for reducing body fat mass so as to prevent lifestyle-related diseases. Lifestyle-related diseases have been...
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StartPage 226
SubjectTerms Body fat mass
Calcium
Diseases
Fats
Feature extraction
Gaussian distribution
Inductive Logic Programming
Logic programming
Nutrients
Statistical analysis
Time-series data
Title Extracting time-oriented relationships of nutrients to losing body fat mass using inductive logic programming
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