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JACIII Vol.21 No.3 pp. 474-482
doi: 10.20965/jaciii.2017.p0474
(2017)

Paper:

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A Chronic Disease Diet Recommendation System Based on Domain Ontology and Decision Tree

Rung-Ching Chen*, Chung-Yi Huang*,**, and Yu-Hsien Ting*

*Department of Information Management, Chaoyang University of Technology
168 Jifeng E. Rd., Wufeng District, Taichung 41349, Taiwan

**Library, Chienkuo Technology University
No.1, ChiehShou N. Rd., Changhua City 50094, Taiwan

Received:
March 28, 2016
Accepted:
January 4, 2017
Online released:
May 19, 2017
Published:
May 20, 2017
Creative Commons License
Keywords:
ontology, decision tree, chronic, nutrients, dietary recommendation
Abstract
As society develops and science and technology improve, people have come to care more about a healthy diet. Diet types have gradually changed and focused more on health management. Taiwan is becoming an aging society in which individuals have irregular lifestyles, long-term unhealthy diets, stressful work, and chronic diseases such as diabetes, hypertension, and high cholesterol. However, most dietary recommendation systems cannot give dietary recommendations for patients with chronic diseases. Though healthy foods are recommended, the systems contain little information on whether nutrients are in balance. Therefore, this study constructed a diet recommendation system for chronic diseases using expert knowledge, which enables more convenient and precise dietary recommendations for chronic diseases. In this study, we use an ontology, decision trees, and Jena to construct the recommendation system. The dietary recommendations results are evaluated by dietitians, and the verification accuracy is 100%. Therefore, this system of dietary recommendations can provide convenient, healthy, dietary recommendations for nutrients for patients with chronic diseases.
Cite this article as:
R. Chen, C. Huang, and Y. Ting, “A Chronic Disease Diet Recommendation System Based on Domain Ontology and Decision Tree,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.3, pp. 474-482, 2017.
Data files:
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