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Mining of nutritional ingredients in food for disease analysis

Lei, Zhenfeng, Zhang, Defu, Liu, Han, Aslam, Saba, Liu, Jinyu and Tekle, Halefom 2018. Mining of nutritional ingredients in food for disease analysis. IEEE Access 6 , pp. 52766-52778. 10.1109/ACCESS.2018.2866389

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Abstract

Suitable nutritional diets have been widely recognized as important measures to prevent and control non-communicable diseases (NCDs). However, there is little research on nutritional ingredients in food now, which are beneficial to the rehabilitation of NCDs. In this paper, we profoundly analyzed the relationship between nutritional ingredients and diseases by using data mining methods. First, more than 7,000 diseases were obtained and we collected the recommended food and taboo food for each disease. Then, referring to the China Food Nutrition, we used noise-intensity and information entropy to find out which nutritional ingredients can exert positive effects on diseases. Finally, we proposed an improved algorithm named CVNDA_Red based on rough sets to select the corresponding core ingredients from the positive nutritional ingredients. To the best of our knowledge, this is the first study to discuss the relationship between nutritional ingredients in food and diseases through data mining based on rough set theory in China. The experiments on real-life data show that our method based on data mining improves the performance compared with the traditional statistical approach, with the precision of 1.682. Additionally, for some common diseases such as Diabetes, Hypertension and Heart disease, our work is able to identify correctly the first two or three nutritional ingredients in food that can benefit the rehabilitation of those diseases. These experimental results demonstrate the effectiveness of applying data mining in selecting of nutritional ingredients in food for disease analysis.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 2169-3536
Funders: National Natural Science Foundation of China
Date of First Compliant Deposit: 3 September 2018
Date of Acceptance: 16 August 2018
Last Modified: 27 Jun 2019 11:31
URI: http://orca-mwe.cf.ac.uk/id/eprint/114535

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