Methods Inf Med 2005; 44(05): 655-664
DOI: 10.1055/s-0038-1634022
Original Article
Schattauer GmbH

A Novel Artificial Intelligence Method for Weekly Dietary Menu Planning

B. Gaál
1   Department of Information Systems, University of Veszprém, Hungary
,
I. Vassányi
1   Department of Information Systems, University of Veszprém, Hungary
,
G. Kozmann
1   Department of Information Systems, University of Veszprém, Hungary
› Author Affiliations
Further Information

Publication History

Received: 29 July 2004

accepted: 16 May 2005

Publication Date:
07 February 2018 (online)

Summary

Objectives: Menu planning is an important part of per-sonalized lifestyle counseling. The paper describes the results of an automated menu generator (MenuGene) of the web-based lifestyle counseling system Cordelia that provides personalized advice to prevent cardiovascular diseases.

Methods: The menu generator uses genetic algorithms to prepare weekly menus for web users. The objectives are derived from personal medical data collected via forms in Cordelia, combined with general nutritional guidelines. The weekly menu is modeled as a multilevel structure.

Results: Results show that the genetic algorithm-based method succeeds in planning dietary menus that satisfy strict numerical constraints on every nutritional level (meal, daily basis, weekly basis). The rule-based assessment proved capable of manipulating the mean occurrence of the nutritional components thus providing a method for adjusting the variety and harmony of the menu plans.

Conclusions: By splitting the problem into well determined sub-problems, weekly menu plans that satisfy nutritional constraints and have well assorted components can be generated with the same method that is for daily and meal plan generation.

 
  • References

  • 1 The interactive menu planner of the National Heart, Lung, and Blood Institute at http://hin.nhlbi.nih.gov/menuplanner/ [Verified April 11, 2005]
  • 2 The Cordelia Dietary and Lifestyle counseling project at http://cordelia.vein.hu/ [Verified April 11, 2005]
  • 3 Balintfy JL. Menu Planning by Computer. Communications of the ACM, vol. 7, no. 4 255-9. April, 1964
  • 4 Eckstein EF. Menu planning by computer: the random approach. J Am Diet Assoc 1967; 51 (06) 529-33.
  • 5 Hinrichs RR. Problem Solving in Open Worlds: A Case Study in Design. Northvale, NJ: Erlbaum; 1992
  • 6 Marling CR, Petot GJ, Sterling LS. Integrating Case-Based and Rule-Based Reasoning to Meet Multiple Design Constraints. Computational Intelligenc 1999; 15: 308-12.
  • 7 GJPetot CR Marling, Sterling L. An artificial intelligence system for computer-assisted menu planning. Journal of the American Dietetic Association 1998; 98: 1009-14.
  • 8 Kovacic KJ. Using common-sense knowledge for computer menu planning [PhD dissertation]. Cleveland, Ohio: Case Western Reserve University; 1995
  • 9 Khan AS, Hoffmann A. An advanced artificial intelligence tool for menu design. Nutr Health 2003; 17 (01) 43-53.
  • 10 Khan AS, Hoffmann A. Building a case-based diet recommendation system without a knowledge engineer. Artif Intell Med 2003; 27 (02) 155-79.
  • 11 Noah S, Abdullah S, Shahar S, Abdul-Hamid H, Khairudin N, Yusoff M, Ghazali R, Mohd-Yusoff N, Shafii N, Abdul-Manaf Z. Diet Pal: A Web- Based Dietary Menu-Generating and Management System. Journal of Medical Internet Research 2004; 6 (01) e4
  • 12 Dollahite J, Franklin D, McNew R. Problems encountered in meeting the Recommended Dietary Allowances for menus designed according to the Dietary Guidelines for Americans. J Am Diet Assoc 1995; 95 (03) 341-4. 347; quiz 345-6
  • 13 Food and Nutrition Board (FNB), Institute of Medicine (IOM): Dietary Reference Intakes: Applications in Dietary Planning. Washington, DC: National Academy Press; 2003
  • 14 Food and Nutrition Board (FNB), Institute of Medicine (IOM): Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients). Washington, DC: National Academy Press; 2002
  • 15 Bucolo M, Fortuna L, Frasca M, La Rosa M, Virzi MC, Shannahoff-Khalsa D. A nonlinear circuit architecture for magnetoencephalographic signal analysis. Methods Inf Med 2004; 43 (01) 89-93.
  • 16 Laurikkala J, Juhola M, Lammi S, Viikki K. Comparison of genetic algorithms and other classification methods in the diagnosis of female urinary incontinence. Methods Inf Med 1999; 38 (02) 125-31.
  • 17 Pena-Reyes CA, Sipper M. Evolutionary computation in medicine: an overview. Artificial Intelligence in Medicine 2000; 19: 1-23.
  • 18 Heckerling PS, Gerber BS, Tape TG, Wigton RS. Selection of Predictor Variables for Pneumonia Using Neural Networks and Genetic Algorithms. Methods Inf Med 2005; 44: 89-97.
  • 19 Coello Coello CA. A comprehensive survey of evolutionary-based multiobjective optimization techniques, Int J Knowledge Inform Syst. 1999; 1: 269-309.
  • 20 Multi-level Multi-objective Genetic Algorithm Using Entropy to Preserve Diversity. EMO 2003, LNCS 2632, 2003 148-61.
  • 21 The M. I.T. GALib C++ Library of Genetic Algorithm Components at http://lancet.mit.edu/ga/ [verified April 11, 2005]