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RGP: Robust Goal Programming for Healthy Nutrition Tracking Using Patients’ Dietary Image Predicted Data

In: AI, Society and Digital Transformation

Author

Listed:
  • Haiyan Yu

    (Chongqing University of Posts and Telecommunications, Key Laboratory of Big Data Intelligent Computing)

  • Zheng Zeng

    (Chongqing University of Posts and Telecommunications, Key Laboratory of Big Data Intelligent Computing)

  • Kun Zhang

    (Chongqing University of Posts and Telecommunications, Key Laboratory of Big Data Intelligent Computing)

  • Zhiqi Chen

    (Shanghai Jiading District Central Hospital)

  • Renying Xu

    (Shanghai Jiaotong University, Renji Hospital, School of Medicine)

  • Senlin Li

    (Chongqing University of Posts and Telecommunications, Key Laboratory of Big Data Intelligent Computing)

  • Jianbin Chen

    (Beijing Institute of Technology, School of Mathematics and Statistics)

  • Ehsan Hajizadeh

    (Amirkabir University of Technology, Department of Industrial Engineering and Management Systems)

Abstract

Dietary nutrition tracking is crucial for personalized healthcare. Traditional methods, including weighing, dietary review, and food frequency, have limitations of low operational efficiency, availability for small samples, and noise from the patient’s memory. Thus, it is essential to have an efficient and accurate method for tracking patients’ nutrition. First, we utilized the U-net-based semantic segmentation method to complete the deep learning process with dietary food images, encompassing feature extraction, category recognition, volume estimation, and weight data collection of patient foods. Second, a data-driven goal optimization model is built to achieve the multiple nutrient intake goals and balance the body’s energy requirements. Furthermore, considering the uncertainty set of dietary nutrition, a data-driven robust optimization algorithm is used to solve the optimization model under noisy data. Finally, we verified the effectiveness and robustness of the proposed method with real-world nutrition data, advancing the personalized services of nutrition tracking.

Suggested Citation

  • Haiyan Yu & Zheng Zeng & Kun Zhang & Zhiqi Chen & Renying Xu & Senlin Li & Jianbin Chen & Ehsan Hajizadeh, 2026. "RGP: Robust Goal Programming for Healthy Nutrition Tracking Using Patients’ Dietary Image Predicted Data," Lecture Notes in Operations Research, in: Xiaolei Xie & Kejia Hu & Guiping Hu & Weiwei Chen & Robin Qiu (ed.), AI, Society and Digital Transformation, pages 304-313, Springer.
    • Handle: RePEc:spr:lnopch:978-3-032-13116-4_24
    DOI: 10.1007/978-3-032-13116-4_24
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