A Survey on Healthy Food Decision Influences Through Technological Innovations

Marshall, Jermaine; Jiménez-Pazmino, Priscilla; Metoyer, Ronald; Chawla, Nitesh V.

doi:10.1145/3494580

Cited by 7 publications

(4 citation statements)

References 68 publications

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“…There are numerous kinds of food recommender services in healthcare domain as reviewed in References 5,6. Table 1 discusses some most recent food recommender services designed to be exploited by dieticians or health enthusiasts.…”

Section: Existing Literaturementioning

confidence: 99%

Cloud menu: Cloud based network analysis for disease‐diet associations and recommendations

Toor,

Chana

2024

Concurrency and Computation

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SummaryFood is one of the most underrated entities with respect to diseases. Food or Diet plays a vital role in healing or recovery of a patient which brings forth the need of analyzing disease and diets associations. The study of such associations is crucial for recommending appropriate diets to patients but is an arduous task due to the complex interdependencies as is evident in literature. Thus, it becomes necessary to automate the analysis and make it available as a service. The main aim of this work is to efficiently collate and analyze disease‐diet associations and provide it as an accessible and adaptable service using a combination of advanced techniques. Complex disease‐diet interdependencies are curated from the literature and transformed into a network offering various parameters for further analysis. The analysis is done using machine learning algorithms to predict accurate and robust recommendations. Cloud computing aids this analysis by providing sufficient resources and making it more accessible. Thus, the recommendations are constructed using amalgamation of techniques including network analysis, machine learning, and cloud computing. The deduced associations from the analysis of medical data using these technologies would aid doctors and healthcare institutions in decision making thereby improving the prognosis of a disease.

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Section: Existing Literaturementioning

confidence: 99%

Cloud menu: Cloud based network analysis for disease‐diet associations and recommendations

Toor,

Chana

2024

Concurrency and Computation

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“…Little effort has been done so far in this direction (e.g. [14,33,40]), which is partially due to the need for long-term user studies in order to evaluate the effectiveness of a proposed approach.…”

Section: Health-aware Food Recommendationmentioning

confidence: 99%

“…Image classification tries to analyse a dish or single ingredient and connect that with additional information to compute a meaningful recommendation [54]. Restaurant recommendations are focused on giving the user a fitting recommendation on where to eat or order next, usually based on the restaurants' menus [32]. Ingredient substitution is interested in modifying dishes or recipes in a healthy manner [47].…”

Section: Introductionmentioning

confidence: 99%

HUMMUS: A Linked, Healthiness-Aware, User-centered and Argument-Enabling Recipe Data Set for Recommendation

Bölz,

Nurbakova,

Calabretto

et al. 2023

Proceedings of the 17th ACM Conference on Recommender Systems

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The overweight and obesity rate is increasing for decades worldwide. Healthy nutrition is, besides education and physical activity, one of the various keys to tackle this issue. In an effort to increase the availability of digital, healthy recommendations, the scientific area of food recommendation extends its focus from the accuracy of the recommendations to beyond-accuracy goals like transparency and healthiness. To address this issue a data basis is required, which in the ideal case encompasses user-item interactions like ratings and reviews, food-related information such as recipe details, nutritional data, and in the best case additional data which describes the food items and their relations semantically. Though several recipe recommendation data sets exist, to the best of our knowledge, a holistic large-scale healthiness-aware and connected data sets have not been made available yet. The lack of such data could partially explain the poor popularity of the topic of healthy food recommendation when compared to the domain of movie recommendation. In this paper, we show that taking into account only user-item interactions is not sufficient for a recommendation. To close this gap, we propose a connected data set called HUMMUS (Health-aware User-centered recoMMendation and argUment-enabling data Set) collected from Food.com containing multiple features including rich nutrient information, text reviews, and ratings, enriched by the authors with extra features such as Nutri-scores and connections to semantic data like the FoodKG and the FoodOn ontology. We hope that these data will contribute to the healthy food recommendation domain.

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“…It is widely recognized that the intake of food lacking in nutritional value has a profound impact on the prevalence of diseases related to diet and lifestyle (Marshall, Jimenez -Pazmino, Metoyer, & Chawla, 2022). The absence of regular physical exertion, combined with the adoption of detrimental dietary habits, has precipitated a surge in health afflictions such as diabetes, cardiovascular diseases, cerebrovascular incidents, diverse forms of cancer, excessive weight, and obesity (Gonçalves, Coelho,…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Food Recommendation System for Dine-in Customers with Non-Communicable Diseases History

Imantho,

Seminar,

Damayanthi

et al. 2024

JTEP

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The rising prevalence of diet-related diseases necessitates a focus on individual food selection to enhance nutrition intake and promote overall health. This study introduces a novel food recommender system utilizing artificial intelligence, specifically a genetic algorithm (GA), to intelligently match diverse nutritional needs with available food items. The research incorporates machine learning methodologies, such as collaborative and content-based filtering, to develop a recommendation model. Data from a commercial restaurant, Nutrisurvey, and the Indonesian food composition list inform the nutritional analysis of five menu items. Consumer variability, considering factors like sex, body mass index, medical conditions, and physical activity, are integrated into the GA framework for personalized food pattern matching. The presented results demonstrate the efficacy of the proposed model in offering tailored food recommendations for consumers with non-communicable diseases (NCDs), such as diabetes, hypertension, and heart disease. The multi-objective optimization technique employed in the system ensures a balance between nutritional adequacy and individual preferences. The presented GA-based approach holds promise for promoting healthier food choices tailored to individual needs, contributing to the broader goal of fostering a sustainable and personalized food system.

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A Survey on Healthy Food Decision Influences Through Technological Innovations

Cited by 7 publications

References 68 publications

Cloud menu: Cloud based network analysis for disease‐diet associations and recommendations

Cloud menu: Cloud based network analysis for disease‐diet associations and recommendations

HUMMUS: A Linked, Healthiness-Aware, User-centered and Argument-Enabling Recipe Data Set for Recommendation

An Intelligent Food Recommendation System for Dine-in Customers with Non-Communicable Diseases History

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