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91

Case-based Diabetic Dietary Plan using Memory Organization Packets

Nurkhairizan Khairudin1, Shahrul Azman Mohd Noah

2, Azilawati Azizan

1 and Ahmad Bakhtiar Jelani

1

1Information Retrieval Research Group

Faculty of Computer & Mathematical Science

Universiti Teknologi MARA, Malaysia

nurkh098@perak.uitm.edu.my

2Knowledge Technology Research Group

Faculty of Information Science & Technology

Universiti Kebangsaan Malaysia

samn@ftsm.ukm.my

Abstract— Planning suitable menus for individuals who have

health problems is a complex task since the solutions produced

by professionals (dietitians) are usually very subjective and

difficult to be systematically represented. This research

focused on designing the knowledge base repository

construction which consists of the existing cases to be used as

the case-based for the retrieval process. The main aim of this

research is, to design dietary knowledge representation base on

set of rules, knowledge and experience adhered by human

experts. Memory Organization Packet or MOP is proposed to

be used along with the Similarity Measure for the matching

process of pass cases from the case-based with the new cases.

This research used actual data and cases which were obtained

from Hospital Universiti Kebangsaan Malaysia (HUKM) and

subsequently evaluated to determine its effectiveness.

Keywords—Memory Organization Packet, Menu Planning

I. INTRODUCTION

A healthy menu construction is an important task for

individuals who have health problem especially for the

diabetic since they need to plan menus within certain

constraint such as consistent calories requirement and also

lots of food to be avoided. Even though generally they are

exposed with lots of information on the healthy diet, but the

ability to generate good combination of food so it can be in

a balance composition rarely can be done by those who do

not have knowledge on diet. In those cases, they seriously

need advice and guidance from the professional in order to

follow accordingly to the planned diet due to certain

constraints such as medical conditions and the daily dietary

recall.

There are applications and approaches available for the

menu construction and dietary analysis. Some are based on

linear programming, genetic algorithms, rule-based or case-

based expert system and they are lots of commercial menu

planning systems exist and are available[1].

Previously, we have already develop an application[2] that

allow user to select food one by one based on their

preferences and then to optimize its assigned amount by

algorithm. This algorithm is design based on the observation

while the dietitians have consultation with patients. But the

defect is to require users to have more expertise and the

guidance from the dietitian is still needed. So the application

can only practical be used by the dietitian but not to the

patient itself.

In this paper, we propose a model of knowledge

representation of case-based approach for diabetic menu

planning. By using this approach, an expert system is

proposed to be developing by extending the previous

version of the application. An expert system is a program

which organizes and presents pre-existing knowledge and it

can mimic expert thinking, judging and reasoning process to

solve the problems[3].

In our case-based reasoning approach, the existing an

successful results are considered as knowledge. In Section

II, we explain on the knowledge acquisition and

representation namely the MOP (Memory Organization

Packets) to be used with the case-based approach. Section

III shows the design of the case retrieval and matching

algorithm and also the implementation.

II. KNOWLEDGE ACQUISITION AND REPRESENTATION

Memory Organization Packets or MOP , introduced by

Shank[4][5] is used as the case representation for the

proposed menu planning system. MOP is a basic memory

model in a dynamic memory for this case-based which

means every item stored in the case-based conducted by

using MOP structure.

The case representation is developed using LISP because of

the appropriateness supports the MOP structure. Koide &

Kawamura[5] also stated that the program complexity of

developing the case-based can be reduced by using LISP

Programming Language.

Menu suggestion for diabetic is different by individual and

they are defined base on several groups and classifications:

1. age, sex, weight, height and individual activity

level.

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2. Diabetes type (IDDM/NIDDM/GDM)

3. Medication/insulin : type of dose and time to

consume the medication.

4. Individual dietary requirement

Therefore, a precise fact for a diabetic menu is important in

order to overcome problem and implication related to this

disease.

The development of the case structure is divided into three

parts which are problem feature, solution feature and result

as shown in the Fig. 1.

CASE STRUCTURE

PROBLEM FEATURES

SOLUTION FEATURES

RESULT

Figure 1. The Case Structure

Problem features is used as the indexes to determine the

type of solution for the problems. The purpose on indexing

is to determine when the cases will be selected to be match

with the new problem and situation. Indexing in a MOP

hierarchy is efficient because of the existing of link create

relationship among the MOPs. The link play an important

role to determine information retrieved.

Set of indexing rules is use in order to determine assumption

feature from the input that will be the suitable index to the

cases in the case-based. All items in the problem feature are

used as the index. This method is call syntax-based method.

This means each case will have many indexes. To simplify

the retrieval process, this problem features is divided into

two categories : personal information and dietary

information as shown in Fig. 2.

PROBLEM FEATURE

KES-PN-Z1

SOLUTION FEATURER

RESULT

OLD CASES

. . .

. . . . . .

:umur 56

:jantina perempuan

:bangsa melayu

:kep-kalori 1500

:kat-bmi

obes-tahap-1

:kand-mpg roti

:kand-spg kueh

:kand-mth nasi-ikan

:kand-sptg tiada

:kand-mml nasi-

ayam

:kand-smml tiada

. . .

INDEX(Perso

nal Info)

INDEX

(Dietary Info)

Figure 2. Index for Personal Information and Dietary Information.

The second part of the case structure is the solution features

which consist of attribute-value pairs that consist of the

solution based on the case index. The attribute-value pair

format for the solution features are as Fig. 3.

:saran-kalori 1600

:menu-mpg mpg-1600-4-roti-1

:menu-mth mth-1600-4-nasi-ikan-1

:menu-mml mml-1600-4-nasi-ayam-1

attribute value

SOLUTION FEATURE

Figure 3. The Solution Features

Fig. 4 shows the MOP maintenance application interface.

This application found to be very helpful in order to manage

the MOP.

Figure 4. Mop structure generated by the MOP Maintenance Application.

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III. CASE RETRIEVAL AND MATCHING ALGORITHM

Case retrieval is a process where retrieval algorithm will try

to retrieve the most suitable case based on the indexed. This

activity will involve the process of searching and matching.

The process of similarity assessment implement an approach

called Nearest Neighbor with Similarity Measure (1).

(1)

Where :

I is the new case (input case).

R is the old case (retrieved case from the case-base).

s is the similarity function for individual feature I and R.

i is the individual feature from 1 to n

wi is the important weighting of feature i.

n is the number of features in each case.

Fig. 5 shows the MATCHING Process and follows by the

algorithm that explain every steps taken until it reach to new

menu generation from the case-based which has the best

match.

RETRIEVAL PROCESS

INITIAL MATCH

SEARCHING

SELECTION

EXACT MATCH

MENU S

UGGESTION

KEYWORD

MATCH

SIMILARITY

MEASURE

Y

T

Figure 5 : The MATCHING Process

1.0 MATCHING Start

2.0 If all IFEATURES match RFEATURES

2.1 BEST MATCH RMENU

3.0 If several IFEATURES match RFEATURES

3.1 IINDEX Classification

3.2 Calculate IINDEX for SIMILARITY MEASURE, S

3.2.1 If S - IINDEX is 1 : BEST MATCH

3.2.1.1 BEST MATCH RMENU

3.2.2 If S - IINDEX nearest to 1

3.2.2.1 Matching process based on

keywords, food group and rank

successful frequencies of case

retrieval is done to the dietary

index.

3.2.2.2 Suggest RMENU for the BEST

MATCH case.

4.0 MATCHING End.

While calculating the similarity measure, it is impossible for

the new problem to return 0 (not match) because the

measurement is done based on the index for demographic of

the patient.

In this research, value for each feature is given based on the

observation of consultation process that has been done by

the dietitian and patient. This value then has been given to

the dietitian to be reviewed before its going to be applied in

the application. The value of weighting is based on the

influences in the generation of dietary menu planning that

has been suggested by the dietitian. Sample of weighting

used are shown in Fig. 6.

Under

WeightNormal Pre-Obes

Obes

Level 1

Obes

Level 2

Obes

Level 3

0.8

0.4

0.8 0.8 0.8 0.8 0.8

0.6

0.2

New Case Old Case

MALAY INDIAN

CHINEESE

OTHERS

0.8

0.6 0.4

0.20.20.2

Figure 6. Sample Weighting for BMI and Races.

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Simulation of similarity measure calculation within the

problem and pass cases is shown in Fig. 7.

Figure 7. Implementation of Similarity Measure for the Retrieval.

Based on the Fig. 7, old case 2 has the value of similarity

nearest to 1. Case 2 is said to be the best match with the

problem.

IV. RESULT AND FINDINGS

There are 15 test cases with 16 suitable criteria of menu are

used to test the effectiveness of the proposed approach.

These test cases were derived from a series of consultation

conducted by dietitians and patients at the Universiti

Kebangsaan Malaysia Hospital. Output produced from the

system is evaluated by dietitians by giving a value between

a scale of 0 to 5. Zero means not suitable and 5 most

suitable.

The result shows most of the criteria are in scale 4 out of 5

for all test cases. In detail, there are only 4 test cases do not

achieve 100% suitability with the suggested menu produce

by this approach but the scale given are still in the average

range of 3.80 and above.

V. CONCLUSION

Knowledge representation is an important aspect for the

system since the effectiveness of the result is totally

effected by the way the knowledge is represented. The

ability of MOP to learn new knowledge in the process of

understanding and solving the current problem make it as a

suitable structure for the dynamic memory.

This research shows that the used of MOP as the knowledge

representation is suitable to guide and plan a healthy menu

with all the constraint of diabetic patient that’s has to be

strict with the calories intake. But there are still limitations

where the case-based must have menus from variety level of

age since most of the cases collected from this research are

from patient age 50 and above. The knowledge

representation generated from this research will be used for

the dietary planner for the diabetic patient and also can be

extend to other diseases based on the constraint stated for

the healthy menus for the patient.

REFERENCES

[1] G. Kov, “Developing an Expert System for Diet Recommendation,”

Methods, 2011, pp. 505-509.

[2] S. A. Noah, S. N. Abdullah, S. Shahar, H. Abdul-Hamid, N. Khairudin, M. Yusoff, R. Ghazali, N. Mohd-Yusoff, N. S. Shafii, and Z. Abdul-Manaf,

“Dietpal: A web-based dietary menu-generating and management system,”

Med Internet Res, vol. 6, no. 1, p. e4, 2004. [3] C. Li and G. Wang, “Study on the Knowledge Base Repository

Construction of Dietary Decision-Making Input Mode,” 2010 International

Conference on Intelligent Computation Technology and Automation, May. 2010, pp. 606-608.

[4] Schank, R. 1982. Dynamic memory; a theory of reminding and learning

in computers. and people. Cambridge University Press. [5] Kolodner, J. Maintaining organization in a dynamic long-term memory.

Cognitive Science. 1983 . Volume 7: 243-280.

[6] S. Koide and I.-harima H.I. Co, “An Implementation of Case-based Memory of an Interface Agent by Lisp , Java , and C ++,” Science And

Technology, 2000. pp. 1-8. [Online] available :

http://www.franz.com/services/conferences_seminars/jlugm00/conference/Talk08_Koide.pdf.