Smart Nutrition Advisor

References

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@article{1708582,
      author = {Derangula Niharika, Aerra Sindhu, Palle Karthik, Keerthi Pendam},
      title = {Smart Nutrition Advisor},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {11},
      pages = {1521-1527},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708582.pdf},
      abstract = {Maintaining a balanced diet in this busy world is a tough challenge. Traditional diet planning methods are usually inflexible and lack personalization. The existing system like nutrisense, mulnutri are flexible but only for one category of people. They used KNN, decision tree etc to solve this problem, and their accuracies are 81%.So,Our proposed system that utilizes machine learning to design meal suggestions based on individual following a normal diet, pregnant women, and individuals with conditions such as high blood pressure (BP) and diabetes. By implementing Random Forest Classifier and XGboost, the system recommended the most appropriate meals for breakfast, lunch, and dinner. This is done through the dataset, which undergoes structured preprocessing, which includes categorical encoding and feature selection, to increase accuracy. The accuracy of this model is 96% benefits of adaptability, automation, and efficiency with our method compared with the static meal-planning systems known so far. The results showed that machine learning can be beneficial in making meal planning more efficient.},
      keywords = {Smart Nutrition Advisor, Balanced diet, Machine learning, Random Forest classifier, XGBoost, Polynomial Regression, meal planning.},
      month = {May},
  }