Mining of Nutritional Ingredients in Food for Disease Analysis

Lei, Zhenfeng; Yang, Shuangyuan; Han, Lujia; Aslam, Saba; Liu, Jinyu; Tekle, Halefom; Bugingo, Emmanuel; Zhang, Defu

doi:10.1109/access.2018.2866389

Cited by 9 publications

(9 citation statements)

References 25 publications

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“…According to common medical knowledge, excessive intake of sodium and fat is one of the risk factors for hypertension. Lei et al [19] analysed the nutritional ingredients affecting hypertension through data mining technology: sodium, carbohydrate, magnesium, calcium, fibre, and potassium, the first four of the nutritional ingredients are the same as our analysis.…”

Section: Resultsmentioning

confidence: 99%

“…However, the existing research mainly focused on the study of factors affecting hypertension on medical and other general data. For the issues of the relationship between the nutritional ingredients and hypertension, Lei et al [19] profoundly analysed the relationship between nutritional ingredients and NCDs by using data mining methods. They proposed an improved algorithm named CVNDA‐R based on rough sets to analyse the recommended food to obtain the top three nutrients that have a positive impact on each disease.…”

Section: Related Workmentioning

confidence: 99%

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Exploring the relationship between hypertension and nutritional ingredients intake with machine learning

Liu

Jiang

et al. 2020

Healthcare Technology Letters

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Exploring the relationship between hypertension and nutritional ingredients intake with machine learning

Liu

Jiang

et al. 2020

Healthcare Technology Letters

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“…Positive food nutrition exerts positive effects on certain diseases which that help to cure those diseases. Also, food nutrition may have a negative influence on some diseases which will worsen the severity of certain disease disorders [5]. Hence, it is vital to consider both the positive and negative nutrition while analyzing food nutrition but, most of the existing works haven't considered these types.…”

Section: Introductionmentioning

confidence: 99%

Harnessing LSTM Classifier to Suggest Nutrition Diet for Cancer Patients

Raguvaran¹,

Anandamurugan²,

Rahman³

2023

Intelligent Automation &Amp; Soft Computing

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“…Decision tree as a classification data mining method, was used to generate the prediction model by visualizing the tree to perform predictive analysis of chronic diseases. Z. Lei et al report in [7] of studying the relationship between nutritional ingredients and diseases such as diabetes, hypertension, and heart disease by using data mining methods. They have identified the first two or three nutritional ingredients in food that can benefit the rehabilitation of those diseases.…”

Section: Introductionmentioning

confidence: 99%

Discovery of Association Rules of the Relationship between Food Consumption and Life Style Diseases From Swiss Nutrition’s (MENUCH) Dataset & Multiple Swiss Health Datasets from 1992 To 2012

Lustenberger¹,

Jenzer²,

Einsele³

2022

Artificial Intelligence, Soft Computing and Applications

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This article demonstrates that using data mining methods such as Weighted Association Rule Mining (WARM) on an integrated Swiss database derived from a Swiss national dietary survey (menuCH) and 25 years of Swiss demographical and health data is a powerful way to determine whether a specific population subgroup is at particular risk for developing a lifestyle disease based on its food consumption patterns. The objective of the study was to discover critical food consumption patterns linked with lifestyle diseases known to be strongly tied with food consumption. Food consumption databases from a Swiss national survey menuCH were gathered along with data of large surveys of demographics and health data collected over 25 years from Swiss population conducted by Swiss Federal Office of Public Health (FOPH). These databases were integrated and reported in a previous study as a single integrated database. A data mining method such as WARM was applied to this integrated database. A set of promising rules and their corresponding interpretation was generated. As an example, the found rules of the sample show that the consumption of alcohol in small quantities does not have a negative impact on health, whereas the consumption of vegetables is important for the supply of vitamins of the B group, which help the energy metabolism to pro-vide energy. These vitamins are particularly lacking in alcoholics and should then be taken with supplements. Another finding is that dietary supplements do little specially by diabetes. Applying WARM algorithm was beneficial for this study since no interesting rules were pruned out early and the significance of the rules could be highly increased as compared to a previous study using pure Apriori Algorithm.

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Mining of Nutritional Ingredients in Food for Disease Analysis

Cited by 9 publications

References 25 publications

Exploring the relationship between hypertension and nutritional ingredients intake with machine learning

Exploring the relationship between hypertension and nutritional ingredients intake with machine learning

Harnessing LSTM Classifier to Suggest Nutrition Diet for Cancer Patients

Discovery of Association Rules of the Relationship between Food Consumption and Life Style Diseases From Swiss Nutrition’s (MENUCH) Dataset & Multiple Swiss Health Datasets from 1992 To 2012

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