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A hybrid healthy diet recommender system based on machine learning techniques.

Sara Sweidan1, S S Askar2, Mohamed Abouhawwash3

  • 1Department of Artificial Intelligence, Faculty of Computer and Artificial Intelligence, Benha University, Benha, Egypt; Faculty of Computer Science and Engineering, New Mansoura University, New Mansoura, Egypt.

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This study introduces a novel machine learning system to personalize obesity treatment by accurately estimating calorie needs and creating tailored diet plans. The system enhances patient-provider communication for effective weight management.

Keywords:
CalorieExpert systemMachine learningObesity treatmentRegression

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Area of Science:

  • Biomedical Engineering
  • Data Science
  • Nutritional Science

Background:

  • Obesity is a complex chronic disease linked to various health risks, increasing morbidity and mortality.
  • Effective dietary planning is crucial for obesity treatment but often challenging for patients and healthcare providers.
  • Current approaches may lack personalization, leading to suboptimal weight management outcomes.

Purpose of the Study:

  • To develop a novel system integrating machine learning for personalized obesity treatment.
  • To accurately estimate daily caloric requirements for weight loss.
  • To formulate individualized healthy diet plans based on medical rules.

Main Methods:

  • Utilized machine learning techniques, including Support Vector Regression (SVR), Logistic Regression (LR), and Decision Tree Regression (DTR).
  • Developed a hybrid precision model with minimal parameters for calorie estimation and diet formulation.
  • Preprocessed a real dataset of 15 anthropometric measurements to enhance model performance.

Main Results:

  • Achieved a high correlation (R = 0.985) in estimating required calories from independent measurements.
  • The model accurately calculates essential macronutrient percentages (fats, proteins, carbohydrates) for daily intake.
  • Demonstrated a practical and cost-effective approach for obesity management.

Conclusions:

  • The proposed system offers a valuable tool for healthcare providers and patients in navigating dietary planning for obesity.
  • Accurate calorie estimation and personalized diet plans are facilitated through advanced machine learning models.
  • This approach supports sequential and effective obesity treatment and management strategies.