Abstract
Obesity is a dangerous epidemic worldwide and is the root cause of many diseases. It is difficult for people to have the same diet with an optimized calorie intake as it becomes monotonous and boring. It will be much better if a dynamic diet can be generated depending upon the calories burnt by a person and their current Body Mass Index (BMI). The active diet planner could provide a person with some change regarding the food consumed and, at the same time, regulate the calorie intake depending upon the user’s requirements. Previously proposed models are either focused only on one aspect of the nutritional information of food or on presenting a diet for a specific issue which is presently facing by user. The proposed system utilizes more balanced approach that focuses on most of the nutritional features of food, and can recommend different foods to a user depending on their BMI. The fat, carbohydrate, calorie, and protein content of food and the BMI of the user are considered while preparing the diet chart. K-means clustering is used to cluster food of similar nutritional content, and a random forest classifier is then used to build the model to recommend a diet for the user. The result of the system cannot be compared with a standard metric. Still, some of the factors that influence the performance of the diet recommender system include the truthfulness of the user while providing information to the design and the accuracy at which the parameters for the model had been set. The advantage of the system comes from the fact that the user has more options to choose from within their suitable range.
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Acknowledgments
The authors gratefully acknowledge the Science and Engineering Research Board (SERB), Department of Science & Technology, India, for the financial support through the Mathematical Research Impact Centric Support (MATRICS) scheme (MTR/2019/000542). The authors also acknowledge SASTRA Deemed University, Thanjavur, for extending infrastructural support to carry out this research.
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Vignesh, N., Bhuvaneswari, S., Kotecha, K., Subramaniyaswamy, V. (2023). Hybrid Diet Recommender System Using Machine Learning Technique. In: Abraham, A., Hong, TP., Kotecha, K., Ma, K., Manghirmalani Mishra, P., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2022. Lecture Notes in Networks and Systems, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-031-27409-1_10
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