This document discusses using case-based reasoning for diet menu planning. It provides an overview of related work using CBR for domains like psychiatry and Alzheimer's care. It then describes DietMaster, a knowledge-intensive CBR system for diet menu planning that uses domain knowledge from experts. DietMaster's architecture and functional components are explained, including its representation of knowledge through rules and cases. Finally, the structures for input cases, cases in process, and output/learned cases are defined.

1. Diet Menu planning Using
Case Based Reasoning
R. Aker
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Rajendra Akerkar
1
Presented at UKCBR 2009, Cambridge, UK

2. AGENDA
 Introduction
 Related work
 Diet Menu Plan
R. Aker
 DietMaster
rkar
 Example
 Conclusion
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3. INTRODUCTION
 An application of case based reasoning (CBR)
in th di t
i the diet menu planning
l i
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4. RELATED WORK
 MNAOMIA
 operates in the domain of psychiatric eating disorders
disorders.
 assists clinicians with the cognitive tasks of diagnosis,
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treatment planning, patient follow-up and clinical research.
The Auguste project
h
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
 is an effort to provide decision support for planning the
ongoing care of Alzheimer’s Disease (AD) patients.
 The first reported system prototype supports the decision
to prescribe neuroleptic drugs for behavioural problems.
 The prototype is a hybrid system where a CBR part decides
p yp y y p
if a neuroleptic drug is to be given, and a Rule-Based
Reasoning (RBR) part decides which neuroleptic to use.
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5. RELATED WORK
 Cbord
 used in hospitals in Australia and New Zeeland
p
 a query is formed on the basis of desired energy
content of the menu as well as a number of critical
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medical conditions which require special diets, such as
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diabetes.
 does not allow the dietitian to alter a retrieved menu
within the system.
 MIKAS
 a menu construction system that supports individual
requirements and food preferences of a client
q p
 multimodal system combining CBR and RBR.
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6. DIETMASTER
 knowledge-intensive approach
 based
b d on an intensive use of domain knowledge and relevant
i t i fd i k l d d l t
parts of the surrounding world in its problem-solving and
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learning methods.
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 Menu planning process is derived using data
collected from experts
p
 (dietitians, physicians, medical practitioners, and home
science departments of various colleges) and
 literature of the Indian Medical Council
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7. DIET MENU PLANNING PROCESS
 Determine the total calories required for an individual
according to his or her physical state and activities he or she
is d i
i doing.
 Determine the nutrient requirements with regard to
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carbohydrates, proteins, and fat to suit the person’s health
state and overcome the energy requirement
requirement.
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 Determine the proportions of eleven food groups to fulfill
total carbohydrate, protein, and fat requirements as well as
restrictions that should be followed to improve the health
p
state.
 Divide these proportions into multiple intakes within a day
(say, 12 hours) period.
 Plan a sample menu by assigning proper food items to each
intake, which will match the food group requirement.
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8. DIETMASTER ARCHITECTURE
Reasoning
New
Problem Planning Task
Control
C l
Combined RBR CBR
Reasoning Control
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MBR
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Solved Rules Cases
Problem
Models
Sustained Learning RBL
Control CBL  RBR – Rule Based Reasoning,
 CBL – Case Based Learning,
Learning  CBR – Case Based Reasoning,
 RBL – Rule Based Learning
Learning,
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 MBR – Model Based Reasoning

9. FUNCTIONAL COMPONENTS OF
DIETMASTER
 The flow of control and information between
the knowledge base and th processes of
th k l d b d the f
problem solving
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10. REPRESENTATION OF KNOWLEDGE
 There are two types of heuristic rules
 the premise-conclusion rule - called conclusive
rule
 premise-> conclusion statement
 the premise-action rule - called conditioned
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procedure.
premise -> action sequence structure.
i > ti t t
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
 different types of knowledge at the object
and control levels
 Domain knowledge at the object level:
 conceptual domain model in which specific
experiences (past cases) and general
heuristics (premise-conclusion rules) are
integrated.
i t t d
 Explicit knowledge about how to use
domain knowledge for problem solving and
learning is described partly as control level
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concepts, partly as control rules.

11. CASE STRUCTURE
Input Case Case in process Learned Case
Input findings Input findings Relevant findings
Goal Derived findings Case reused for generating a plan
Solution constraints Explanation of successful plan
Planning states Successful diet plan
Possible planning Different cases
Explanation of planning Book-keeping information
Possible diet plan
Explanation of plan
Similar cases
Case base is a set of features of client C = { I,O,R), which is
stored in the form of frame.
R subset represents outcome, it is a knowledge collected
after solving a case by system for further reuse.

12. INPUT CASE
 When diet planning process starts,
 it generates some intermediate results
describing the client in more detail.
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 Those are called as derived descriptors of the
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case, those along with input descriptors
forms case in process.
 After completion of the planning process case
will be stored in the case base for future
reuse.
 Here only required information for reuse gets
stored in the case base this becomes case to
store.
store 12

13. OUTPUT CASE
 This set is a collection of two subsets,
 i.e. calculated results and planning information.
 Subset calculated results consists of
 standard weight,
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 weight state ( under-weight or over-weight),
 total calories required, total calories gain from previous
required
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meal plan, nutrient requirement ( carbohydrates, proteins,
fats requirement) in gm.
 Subset planning information consists of food group
proportion
 i.e. ratio of 11 food groups to contribute total calories
distribution, intake wise food group distribution, restrictions
on food items indicates food items desirable for health
state and food items to be avoided to improve health statestate.
 This also contains menu depicting total number of
intakes per day in detail giving food items with their
quantities for each intake.
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14. OUTCOME
 Consists of attributes made
 for case numbers of previous cases along with their usage
to generate current output,
 matching string giving neighborhood of current case with
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previous reused case, case number allotted to current case
for identification purpose,
p p ,
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 state of case indicating whether meal plan suggested in
output is successful,
 failed or case is under observation.
 This set maintains results of the case which consists
of state of blood glucose level ( improved, aggravated,
or no change), insulin dosage status ( stopped, dosage
reduced, dosage increased or no change) , health
complaints state indicates diseases get cured and
which diseases are aggravated.
 This structure is helpful for multilevel adaptation
method to reuse for solving similar future cases.
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15. CASE STRUCTURE
Age (in years)
Weight (kg) Vegetarian / Non Vegetarian
BFI (W i t t Hi ratio)
(Waist to Hip ti )
Height (cm)
Work type / activity
Gender
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Lose of wt / Bed patient / Sedentary /
Male / Female Moderate / Heavy y
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Female Status
General Test details
BP – Blood Pressure in mm
1st Trimester Pregnancy
g y
Systolic,
S stolic Diastolic
2nd Trimester Pregnancy BGL Test Method
3rd Trimester Pregnancy Plasma / Whole Blood / HbA1c / Urine /
0 – 6 Mon Lactation Glycometer
7 – 12 Mon Lactation BGL - Blood Glucose Level in mg/dl
Fasting BGL , Postprandial BGL
Body Frame
Cholesterol mg/dl
g
Small / Medium / Large Insulin
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Eating Habit

16. CASE STRUCTURE
Type
Dosage for a day in cc
Exercise
(Type, duration in minutes )n
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Diabetic Symptoms
Polyuria
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Polydypsia
Polyphagia
Dehydration
Loss of weight / Weakness
L f i h W k
Less Immunity
Less Healing capacity
Degenerative Changes
Ketosis/Acidosis
Other health complaints
(Disease )m
Current meal P tt
C t l Pattern 16
(Intake, Food item, Unit Quantity)m

17. RULES
 Balanced weight for given height, age and gender has
been stored in terms of associated rules. There are
two sets of rules. One set maintains standard weight
f l O i i d d i h
for children.
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 (Age, Height, Gender)-> Standard Weight.
 This rule checks the physical state of a person and
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decides whether he is under weight, over weight or
obese (recognized with obesity status).
 (Standard Weight, Actual weight, Body Frame, Waist to
Hip ratio)-> Obesity Status
ratio) >
 Restrictions on food groups are also defined as
minimum and maximum levels of food groups, which
suit the health state of the client, and also help to
improve it These are conclusive rules and stored in a
it.
Table.
 (Obesity symptoms, Other health complaints) ->
(Restrictions on food group) n
 (Food
(F d groups are Cereals, Pulses, Milk, Eggs, Meat, Green
C l P l Milk E M t G 17
Veg, Other Veg., Roots & tubers, Fruits, Sugar, Oil)

18. PROCEDURES
 General knowledge is stored in terms of procedures
 solves problem partially whenever past cases are not
available.
il bl
 These procedures are also used in adaptation process.
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 Major five operations in diet menu planning along
with supportive processes are g
ith ti generalized and k t
li d d kept
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available.
 Calculate Total Calories
 CPF Proportion finds nutrient requirement of the
individual
 Food Group Proportion procedure to distribute nutrients
among 11 food groups.
 Food
F d group di t ib ti procedure to distribute food group
distribution d t di t ib t f d
exchanges among different intakes.
 Menu construction
 These can also act as fundamental knowledge and
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some times with conditioned rules.

19. PROBLEM SOLVING
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20. INTEGRATED REASONING
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21. DETAILED CBR
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22. THE LEARNING
ALGORITHM
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23. LEARNING IN DIETMASTER
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24. INDEXING OF CASES
 DietMaster case contains the following information
Relevant findings Successful planning Explanation of successful
planning
differential features Successful diet Explanation of successful
p
treatment treatment
differential cases failed plan
prototype of case failed diet treatment
 a case also contains ’book-keeping’ information
book keeping
 time reference, the number of times it has been used to
solve new problems, etc.

25. TO RETRIEVE MOST APPLICABLE CASE
 Indexed in multiple ways. Those are broadly categorized
in three types:
 Relevant input features : This is the subset of the input
descriptors that has been explained to be relevant for solving
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the problem. If a numeric descriptor was transformed into a
symbolic value, this becomes the value of the index feature.
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 Derived findings : These are findings, which may disqualify a
case, since they point to cases that probably are better matches
if such a finding is involved. Differential findings are inserted in a
involved
case reminded of when this case does not lead to a successful
solution. Hence, they are indices between rather similar cases,
although different enough to associate different solutions.
 Plan : S
Pl Successful as well as failed solutions provide indices to
f l ll f il d l ti id i di t
the cases. The primary use of indices which are successful plan
is for the retrieval of cases in order to find proper intake.
Indices that are failed solutions are used to avoid retrieval of
similar failures while solving new problems. 25

26. REFERENCES
 Akerkar, R. and Sajja, P. Knowledge Based Systems, Jones and
Bartlett, 2009.
 Jamsandekar, P., Akerkar, R. Dietary Planner, A Case Based
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Reasoning System; In proceedings of Recent Trends in IT, A National
Conference, Amaravati, India, 2000.
Co fere ce A aravati I dia 2000
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 Kolodner, J. Case based Reasoning: Morgan Kauffmann Publisher;
San Mateo, CA, 1993.
 Dietary Guidelines for Indians – A Manual, National Institute of
Manual
Nutrition, ICMR, Hydrabad.
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