=Paper=
{{Paper
|id=Vol-3706/Paper1
|storemode=property
|title=Development of a Knowledge-Based System for Diagnosis and Treatment of Obesity
|pdfUrl=https://ceur-ws.org/Vol-3706/Paper1.pdf
|volume=Vol-3706
|authors=Abebaw Agegne,Ayodeji Olalekan Salau,Ayalew Belay,Anupam Singh,Nigus Wereta Asnake,Sepiribo Lucky Braide
|dblpUrl=https://dblp.org/rec/conf/icaids/AgegneSBSAB23
}}
==Development of a Knowledge-Based System for Diagnosis and Treatment of Obesity==
https://ceur-ws.org/Vol-3706/Paper1.pdf
Development of a Knowledge-Based System for
Diagnosis and Treatment of Obesity
Abebaw Agegne1,† , Ayodeji Olalekan Salau2,† , Ayalew Belay3,† , Anupam Singh4,∗,† ,
Nigus Wereta Asnake5,† and Sepiribo Lucky Braide6,†
1
Department of Computer Science, Debark University, Debark, Ethiopia
2
Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria
3
Department of Computer Science, AAU University, Addis Ababa, Ethiopia
4
Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, India
5
Department of Information Technology, Debark University, Debark, Ethiopia
6
Department of Electrical and Electronics Engineering, Rivers State University, Port Harcourt, Nigeria
Abstract
Obesity is a major public health issue that affects both industrialized and developing countries. Obesity
is a varied and complex issue that necessitates diagnosis and treatment. Various research projects have
attempted to create and develop a knowledge-based system (KBS). Physicians’ workload and medical
errors could be reduced by using a KBS. The goal of this study was to create a KBS that can guide
clinicians and patients through the diagnosis and treatment of obesity. A case-based reasoning approach
is applied in this study. Case-based reasoning is a general artificial intelligence paradigm that has
been studied in the context of improving human decision-making and has gotten a lot of interest in
the development of KBSs. To acquire data, a purposive sampling technique is employed. The explicit
knowledge is collected from the literature. To develop the prototype, JCOLIBRI software is used. In the
development of the knowledge base system, 243 obesity cases were used. During evaluation, six experts
were involved. User acceptance testing, case retrieval in terms of precision and recall, and case similarity
testing are all used to assess the system’s performance. The user acceptance testing conducted showed
that 90% of the evaluators found the system to be easy to use, efficient with respect to time and speed
of the system. With precision and recall, the performance of the system is 71% and 80% respectively.
The study found that there is a shortage of physicians and medical experts who can diagnose and treat
obesity. That means that a single physician or medical doctor will not be able to treat a large number of
people in a short amount of time. The proposed system in this study could help to alleviate the shortage
of experts who can diagnose and treat obese people. The system helps to decrease the amount of work
that physicians have to do by allowing them to delegate the same patient cases to other experts.
Keywords
Case-based reasoning (CBR), knowledge-based system (KBS), obesity, cases, artificial intelligence
1. Introduction
In the medical field, a physician’s major responsibilities are diagnosis, categorization, and treat-
ment. Because the medical domain, such as the psycho-physiological domain, is multifaceted
ACI’23: Workshop on Advances in Computational Intelligence at ICAIDS 2023, December 29-30, 2023, Hyderabad, India
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open anupam2007@gmail.com (A. Singh)
Orcid 0000-0002-6264-9783 (A. O. Salau)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
6
and complicated, it frequently needs the development of a system that employs a number of
artificial intelligence techniques. Obesity [1] is one of the psycho-physiological domains in the
medical realm. Obesity has long been recognized as a public health issue, with huge rises in
incidence in both emerging and industrialized countries over the last 30 years dubbed a ”obesity
epidemic.” Obesity is a major public health issue that affects industrialized and developing
countries alike [2].Obesity is a global health problem and affects more billions of people of all
ages and sex [1].
According to WHO, obesity and overweight are both defined by abnormal or excessive fat
buildup that might have harmful health repercussions [3, 4]. The symptoms of obesity are
classified as case problems like short-term problems, long-term problems, and Psychological
problems [5]. The short-term problem symptom is the day-to-day life activity happens like
breathlessness, snoring, increased sweating, difficulty sleeping, and inability to manage sudden
physical activity. The long-term problem symptom is the symptoms that you may not know
but has seriously harmed your health, such as hypertension, diabetes, infertility, and the like
[6, 7]. Psychological problem symptom is a problem to do with mental health like having low
self-esteem, poor self-image (not liking how you look), low confidence levels, feeling isolated
from the society. The major important factor in influencing our weight is lifestyle choices [8].
For example, bad food choices such as processed or fast food that is high in fat, a lack of fruits
and vegetables, excessive alcohol use, low energy expenditure, and heredity. In addition to
unhealthy food choices, the most common cause of obesity is physical inactivity, overeating,
medications, psychological factors, diseases like hypothyroidism, diabetes, pituitary cancer, and
social issues [5] There is no easy cure and no country has yet achieved major reductions in
obesity rates [3]. Excess body weight has considerable impacts on individuals, the health care
system, and society. Only knowing the symptoms is not the treatment of obesity. The diagnosis
should be refined in order to select the most appropriate and effective treatment. A professional
doctor can test obesity treatments with all epidemics and related disorders [3]. As a result,
diagnosing and treating obese patients takes time and is difficult to follow up on. To overcome
such issues, a knowledge base system must be developed and designed [1]. Knowledge-based
systems (KBS) employ intelligent reasoning to solve a problem that would normally necessitate
a significant amount of human work, time, and skill. It provides dependable solutions for daily
decisions and processes, as well as a substantial amount of data. Knowledge-based systems
(KBSs) can reduce the burden of physician and medical errors during the diagnosis and treatment
of patients. KBS are developed by duplicating human intelligence to assist health physicians in
the decision-making process without asking the specialist doctors. The system does not replace
specialists, but it does assist physicians in diagnosing and treating patients. As a result, KBS
appear to be a promising solution for avoiding time wastage and help to minimize medical
failure of a medical system [? ].
According to the WHO, the global burden of deaths of persons having overweight and obesity
is recorded to be 2.8 million deaths per year and 35.8 million disability-adjusted life [? ]. Despite
the sustained high prevalence of under nutrition, overweight and obesity are becoming an
emergent public health hazard in developing countries [1, 8? , 9, 10]. In Africa, the body mass
index (BMI) has risen over time in all regions, following the global trend. Few remarkable
research to diagnose the human diseases using soft computing approaches were published [11,
12, 13, 14, 15].
7
The remaining section of this paper is organized as follows. In section 2, related literature work
is provided, while the methodology is discussed in section 3. In section 4, the model/architecture
of the system is presented. In section 5 and 6, the system evaluation and conclusion are presented
respectively.
2. Related literature work
International and national research on knowledge-based systems (KBSs) is being conducted
to develop alternatives in the field of medical diagnosis [8]. Ndukwe et al. [1] developed an
improved rule-based expert system for the diagnosis of Obesity. The study was conducted
using rule sets developed from getting information from Obesity experts to build a system.
This developed RB system cannot learn and does not support complex domains. This makes
the system manual work. The key limitation of the RB system is inference efficiency problems
i.e. when there was a large rule base. Also, it is not possible to conclude rules when there
are missing values in the input data. So, the case-based reasoning technique is the alternative
solution to develop a KBS. The existing developed rule-based expert system is done for diagnosis
only but does not support the treatment of obesity [1]. It is only applied for diagnosis purposes
with a few cases and features. In addition to this, the previous studies of diagnosis and treatment
of obesity are not learning KBS as most are developed with a rule-based approach. Therefore,
by considering this gap the authors proposed to design a KBS to diagnose and treat obesity.
KBS can help alleviate the shortage of physicians and medical doctors who help to identify
and treat obesity. This aids a single physician or medical doctor can provide care to a large
number of patients in a short amount of time. The solution to this study’s dilemma could help
to alleviate the shortage of experts who can diagnose and treat obese people. It will help reduce
the workload on physicians by allowing them carry out the same patient cases with various
experts.
The remaining section of this paper is organized as follows. In section 2, related literature work
is provided, while the methodology is discussed in section 3. In section 4, the model/architecture
of the system is presented. In section 5 and 6, the system evaluation and conclusion are presented
respectively.
3. Methodology of the study
3.1. Data collection
The relevant information was gathered from both primary and secondary sources in order to
complete this study. Particularly, 243 obesity cases were collected from University of Gondar
referral hospital.
3.2. Sampling method and sample size
For knowledge acquisition purposes, the purposive sampling method is used to select the domain
experts from the University of Gondar referral hospital.
8
4. Knowledge acquisition (KA)
Every knowledge engineer should consider the two basic important steps in knowledge engineer-
ing, which are important through the growth of KBS. The first one is knowledge elicitation from
domain experts and various relevant documents. The second step is represented the collected
knowledge that we get from the domain experts with the appropriate knowledge representation
method [8]. KA is the method of gaining useful information (applicable data) from domain
experts as well as from other sources such as books, research papers, manual/guidelines, and
transfer to the knowledge base (case base. KA is the most important step in KBS development,
but at the same time, it is the most difficult that requires great care, patience, and attention. The
first and most time-consuming phase in the creation of KBS is KA. There are several phases in
the KA process. Some of these are as follows: choosing a problem for the computer to answer,
interviewing an expert, questionnaires, observation, record reviews, codifying the information
in some representation language, and improving the knowledge base by testing and expanding
its capabilities [? ]. During KA, we are interested with how information is collected and where
it occurs, since this impacts the system’s utility [9]. For this study, primary and secondary
sources are required. The knowledge acquisition process is applied to get primary sources (tacit
knowledge) from the domain experts using a semi-structured interview.
4.0.1. Knowledge acquisition from domain expert
In this step, knowledge is gathered from domain experts. Domain experts are professionals and
an experienced people who can solve a problem in a specific domain area. Obtaining knowledge
from domain experts involves understanding how they perform a specific task and describes
what general knowledge they have about the domain area. Interviews are one type of knowledge
elicitation strategy that includes asking domain experts how they accomplish their duties and
achieve success. In the heads of experts (tacit knowledge), the most important knowledge lies.
Tacit knowledge has a life with limits (when the domain expert dies, the knowledge also dies
with the expert). Therefore, to be used and understood by non-experts, the enormous amount
of tacit information in expert minds should be codified and digitized. To collect the relevant
knowledge the authors applied a semi-structured interview technique. Six domain experts from
the domain area were selected and the purposive sampling technique was used. To minimize
the constraints of KA tasks, experts were chosen to optimize the acquisition process based on
their educational credentials, years of experience, and immediate job positions in the domain
field.
4.0.2. Knowledge acquisition from the relevant document
To create KBS as a secondary data source, explicit knowledge from the appropriate document
is gathered. Relevant obesity-related documents were evaluated in order to elicit explicit
information for this study. Books, published articles, Ethiopia Standard Treatment Guidelines
for General Hospital and manuals, and other overweight and obesity treatment guidelines are
among the documents.
9
4.0.3. Knowledge acquisition from obesity cases
After collecting knowledge from domain experts and appropriate papers, the acquired knowledge
from obese cases is used. The domain specialists not only provide their knowledge, but also
assist in the acquisition of knowledge from the obesity record files. These instances have been
discovered in the leprosy ward. Age, sex, family history, BMI, and waist circumference were
among the factors acquired by the writers that were not included in the text. These variables
were acquired with the help of a domain expert from obesity cases at the University of Gondar
referral hospital. These factors are crucial in determining whether or not a patient is obese. All
of these obesity-related factors were gathered from obese patients at the University of Gondar
referral hospital. All of these elements were gathered with the help of domain specialists. The
researcher tried to call the obese patient directly from the hospital, but due to the coronavirus,
no one came to the hospital.
5. Knowledge modeling
The knowledge modeling is performed after gaining the knowledge (data) from domain experts
and related document. It is a critical step in the KA process to know the problem and to plan
the representation steps of the knowledge. In the knowledge acquisition phases, knowledge
engineer collects both tacit and explicit knowledge. The knowledge engineer will attempt
to understand both the tacit and the explicit parts of the knowledge and then represent the
knowledge with diagrams. The knowledge engineer must then create an abstract model of
everything that was addressed throughout the knowledge acquisition step. Different knowledge
modeling strategies exist, such as hierarchical and decision tree structures. The decision tree
structure was chosen for this study to simulate how obesity diagnosis is carried out.
From the Fig. 1, the oval symbol represent the idea of continuing for the next diagnosis. That
is the patients has no psychological symptoms, then the patients test by other criteria when the
patients encounter them.
The symbol has no other meaning, only used as a purpose with the variable X. As shown
in Fig. 1, the diagnosis and treatment of obesity are mostly determined by BMI and waist
circumference. After checking the short-term and long-term symptoms of obesity, the patients
will be checked by measuring their BMI and waist circumference.
5.1. Design the model of CBR for obesity
The next step is to encode the knowledge into computer system after gathering cases and
expertise from domain experts as well as numerous relevant papers. The case structure will be
built when the knowledge engineer obtains the data through interviews and document analysis.
To create the case structure, first decide whether you want simple or compound attributes. After
selecting an attribute type, assign a weight value to each important attribute and select the
appropriate local similarity for each. Following the creation of the case structure, the knowledge
engineer should configure the connectors by selecting from a variety of connector types such
as SQL database, plain text, or other connectors. When a new problem meets the prototype,
the system looks for each characteristic from the entered cases and stored solved cases in the
10
case base to find a retrieved case for the new case. The system computes the local similarity
since JCOLIBRI provides distinct local similarity metrics for each data type. It identifies the
global similarity of the new case with previously solved instances by multiplying the weight of
each attribute with the local similarity calculation result after computing the local similarities.
The prototype system ranks relevant retrieved examples based on their global similarities after
computing global similarities between the current case and previous cases. When we present a
new problem, we fetch the pertinent cases from the case database. If possible, reuse/adaption is
the solution to the preceding instance. Revise is the application of knowledge that frequently
leads to a revision of that knowledge or cases based on the expert’s experience. If the solution
is successful, the solution is retained. From Fig. 1, general knowledge refers to the type of
knowledge such as, vocabulary knowledge, adaptation knowledge, and retrieval knowledge.
When the new case (query) from the user comes, the first search from the case base. If
the search case is exactly matched with the stored cases, use the solved cases directly. If the
retrieved case is a partial match from the case base, use the adapt solution. If the retrieved case
is new (not similar to the case base), by using the adaptation process, determine the solution for
the new case. The adaptation (reuse and revision) means a modification of solutions of former
similar cases to fit for a current one. But, using the suggested solutions directed may have
a risk in diagnosis and treatment condition. Therefore, the expert adapted by modifying the
variations between the old case and the current cases.
In this situation, cases might be maintained based on the result of the proposed solution
for storing purposes. Case variations are updated in addition to adaption if the retrieved case
differs from the new case. Finally, in the case-based reasoning system’s retention phase, the
experience gained from the freshly solved case is saved for future use. As a consequence, the
newly solved obesity case is saved in the case database for future reference.
5.2. CBRS for obesity diagnosis and treatment
To develop the CBRS for obesity diagnosis and treatment, several steps are performed with the
JCOLIBRI tool. Some of these are gathering cases and knowledge of the context, modeling an
appropriate case representation, computing similarity measures, implementation of retrieval
features, and creating user interfaces. To do this, an interdependent task in JCOLIBRI must be
configured first. These are configuring all CBR tasks, methods, connectors, case structure, and
building the case base.
5.2.1. Managing case structure
Managing case structure is the fundamental task in a CBR system in JCOLIBRI. In this task, the
description and solution attribute, cases are defined clearly. Those acquired cases are saved
in a plain text file format. JCOLIBRI generates codes automatically and stores them in XML
file format when creating a case structure. The important attributes are declared with a higher
weight value.
On the right side of the case structure window, as illustrated in Fig. 2, the data type, weight,
and local similarity of the selected attribute may be specified. The right side of the window
contains the attribute’s property values as cases, whilst the left side of the window contains the
11
Figure 1: Architecture of CBRS for the diagnosis and treatment of obesity.
attribute structure as a tree.
5.2.2. Managing connectors in JCOLIBRI
As the name indicates that, the connector can be defined as a link between two or more
things together. As shown below in Fig. 3, the connector connects the case structure and the
knowledge-base or case base (datasets). The use of connectors is to make JCOLIBRI flexible
against the physical storage, therefore users of the system choose the most appropriate one for
12
Figure 2: Snapshot from definition of the case and managing case structure.
the system when they need it. JCOLIBRI holds different connectors with different file formats
like XML, plain text file, relational database file, and CSV files. For the implementation of the
Obesity diagnosis and treatment model, the researcher used plain text connector. Because the
plain text connector is easy to configured the case structure and other JCOLIBRI tasks.
The plain-text file case base connector is used to keep the case in the case base. The connector
maps the case structure to its column from a plain text file stored in.txt format, which is then
saved as an XML file, much like the case structure. The connectors link the case structure
stored with the XML extension to the dataset or cases saved in plain text (.txt) format. The most
fundamental tasks in connection management are specifying the appropriate case structure
and file path. The case structure path is used to access and match case structure characteristics,
whereas the case base.txt file is defined by the file path. A comma (,) is used as the connector’s
delimiter to separate the values of each attribute in the case.
5.2.3. Building case base for obesity cases
In the CBR system, cases play a great role as the source of the dataset. The researcher collects
Obesity patient cases from the University of Gondar Referral Hospital. The gathered instances
are used to create an Obesity diagnostic CBR system that assists physicians and other health
workers by providing decision assistance. All of the obtained cases are saved in attribute-
value representation format as a plain text file. We represent cases by the attribute-values
representation technique because it supports the nearest neighbor retrieval algorithm and
represents cases in a simple manner.
To implement the prototype of the CBR system for Obesity diagnosis and treatment, JCOLIBRI
software is used. COLIBRI tool is selected because of the following essential reasons: JCOLIBRI
represents cases in a very simple way, and also has different functionalities to reflect the infor-
mation gained and organized. It provides a simpler process of development, based on the reuse
13
Figure 3: Snapshot of the configuration of connector in JCOLIBRI
of past designs and implementations. It supports different data types of case structure, which
describes every simple event. The tool supports various knowledge representing techniques. It
supports an effective facility interface with external programs and systems.
JCOLIBRI software mainly includes managing case structure, managing connectors, managing
tasks, and methods of subtasks to solve the problem. The basic thing to describe those managing
steps in the CBR system, first to describe the representation of cases in the case base.
• The Case representation
The case can be defined as an abstract representation of a past problem and its solution.
Cases in CBR have two parts. Problem description, which is the state of the world while
the case is happening and what problem needed solving at the time. The second part is
the Solution, which stated or derived a solution to the problem. Those collections of cases
are represented in different representation techniques. Depending on the system, cases
may be represented as simple plain attribute-value, textual cases, or complex hierarchical
(object-oriented) structures where attributes are connected among them [10]. For this
study, the researcher selected attribute-value pair representation.
• Attribute selection The Cases in CBR are objects of the class described by attributes.
The attributes has a great role in its value and the weights of the attribute govern the
importance of the attribute in the case structure. Attributes with weight zero are not
included in the case base. To select case values from a case-based, the appropriate
attributes or parameters must be selected. Different possible attributes are used to
represent the obesity cases in the medical domain. For this work, 13 attributes are
selected accordingly as presented in Table 1. The attributes are selected by analyzing
14
Table 1
Description of the selected case attribute in JCOLIBR.
Descriptive attributes/Features of obesity
Attribute name Data type Weight Local similarity
Sex String 0.05 Equal
Age Integer 0.05 Equal
Over_eating_fat_foods Boolean 0.08 Equal
Vegetables_Consumption_Frequency String 0.06 Max String
Stress Boolean 0.06 Equal
Sleeping_deprivation Boolean 0.06 Equal
Physical_Activity Boolean 0.06 Equal
Alcohol_consumption String 0.06 Max string
Type_of_transportation_used Boolean 0.06 Equal
BMI Integer 0.2 Interval
Family history Boolean 0.06 Equal
Waist circumference Integer 0.2 Interval
the previous case. The important attributes are used to extract a solution for the new
problem from the CBR system. Since all attributes are not equally significant, assigning
the appropriate weight values is necessary. The assignments of weights to each attribute
indicate that attributes having higher weights are the most important and smaller weights
are, the less important ones to diagnosis and treatment of obesity patients. The weight
of each attribute has been assigned its value by domain experts at the time of attribute
selection.
5.3. Implementing the Prototype of CBR Application
Once all the steps required in JCOLIBRI are defined and configured, the CBR application using
the real Obesity patient’s data is implemented and tested as shown in Fig. 4. The Configuration
and the total CBR task could be saved for future use and modification. After configuring the
case structure, connector, and selected tasks in each CBR cycle, the remaining task is filling the
query interface to store cases in the case base as shown in Fig. 5.
After filling the values in the window interface, the system will retrieve the given query. The
retrieved task starts with a problem description and ends when a best-matching previous case
has been found. This data from the Battery stats helps to enhance energy estimations. The
information gathered is connected to the active components’ present condition, the frequency
of CPU and method call invocation, and the files loaded by the power profile. The power
profile may be used to calculate the current consumption for each component, as well as the
approximate battery drain for each component. This will specify how many milliAmperes
of current are necessary for the CPU to complete a single cycle at a set frequency for each
repetition of execution for each application. These details may be obtained from any smart
phone’s power profile [? ].
Retain in CBR comes after the revision step in the CBR system. When we save the revision
15
Figure 4: Snapshot of each CBR System cycle with their subtasks.
Figure 5: Snapshot of Window for case enter for case base.
cycle, retain window becomes available to store a case in the case base. The retain step in a
CBR system is mainly used to store cases in a case base. The retain step also has the advantage
to insert the new case in the case base. This step has the chance to add the new cases which are
not found in the case base for the new problem that occurred. For example, if there were 34
cases in the case base, we have a chance to add the new case as an additional case for use in the
future. This is exemplified in Fig. 7.
6. Testing and evaluation of the prototype
Following the implementation of the CBRS obesity diagnostic and treatment prototype system,
each CBR system must be tested and reviewed to ensure that the CBRS prototype’s performance
is correct and that the prototype system is useable by end-users. Testing and evaluation of
16
Figure 6: Snapshot for retrieved solution for the new query from the case base.
Figure 7: Snapshot for retain cycle of CBR system for obesity cases.
the prototype system responses to the request ”how the KBS is diagnosed and treat the obese
patient perfectly?”. To solve this issue, user acceptance testing and system performance testing
are employed.
17
6.1. User acceptance testing
The user acceptance testing measures the issues of how the system addresses the needs of the
user from the user’s point of view. To conduct user acceptance testing experts were used. The
user acceptance testing is performed in a real situation at the University of Gondar specialized
hospital for system validity.
Due to the threats of the coronavirus, doctors are so busy to give time for evaluating the
system at the hospital. Therefore, the researcher has selected 2 medical doctor experts and 4
apparent students (5th year medical students) to examine the functions of the prototype by 7
sample questions. For each scale the researcher gives values in number as poor = 1, fair = 2, good
= 3, very good = 4, excellent = 5. Based on the given scale, the selected domain experts give value
to each attribute through the selected queries. This method of testing allows the researcher to
analyze user satisfaction with the prototype system based on interpretations of the user response.
The researcher uses visual interaction assessment along with close-ended questionnaires to
answer the issue of user acceptance. The method of visual interaction assessment enables the
domain expert to comment by interacting with the system and assessing the prototype result.
After assigning the scale value, we can calculate the user acceptance by the following general
formula.
𝑠𝑣1 ∗ 𝑛𝑟 𝑠𝑣2 ∗ 𝑛𝑟 𝑠𝑣3 ∗ 𝑛𝑟
AV = + + +⋯ (1)
𝑡𝑛𝑟 𝑡𝑛𝑟 𝑡𝑛𝑟
To get the result of user acceptance, average performance is calculated out of 100% can be
calculated as:
𝐴𝑉 ∗ 100
AVP = (2)
𝑇𝑆
where AV is average, AVP is the average performances, TS is the total number of scales, i is
the individual scale, nr is the number of respondents who participated in each scale value. Tnr
is the total number of respondents and sv is the scale value. The AV and AVP are calculated,
using the formula given above. For instance, to calculate the average and average performance
the first evaluation criteria ”Easy to use the system” is as follows: average is equal to scale
value multiplied by the number of respondents divided by the total number of the respondent
which is (4*2)/6 +(5*4)/6 = 4.6. To calculate average performance, AVP is equal to average
multiplied by 100 divided by the total number of the respondent that is AVP = (4.6*100)/6 =
92. The remaining evaluation criteria’s AV and AVP are calculated similarly. As indicated in
the above Table 1, 33.3% of the respondents rated to evaluate “easy to use the system” as very
good and the remaining 66.7% as excellent. The overall average performance for this evaluation
criteria is 92%. In the same way, the “system efficient in time” evaluation parameter has the
respondents rated scale is 16.7%, 33.3%, and 50% to the rank good, very good, and excellent
respectively. The overall average performance for this evaluation criteria is 86%. The user
interface interactivity also has the respondent rate as good, very good, and excellent is 16.7%,
50%, and 66.7% respectively. The overall average performance for this evaluation criteria is 84%.
Likewise, the accuracy of the system to categorize patients correctly has the respondent rate
of 33.3%, good, 33.3% very good, and 33.3%, excellent. The total average performance for this
evaluation criteria is 80%. In the same manner, the ”effectiveness of the system to time and cost
18
for patient” evaluation criteria have the respondent rated 33.3% very good and 66.7% excellent.
The total average performance for this evaluation criteria has been recorded at 96%. Also, the”
system applicable to medical domain” evaluation criteria has a response rate of 33.3% very good,
and 66.7%. The total average performance for this evaluation criteria is 92%. Lastly, ”the speed
of the system” has a response rate is 100% and its total average performance is also 100%.
6.2. System performance testing with cases retrieved
Precision and recall are used to evaluate the performance of the prototype system practically.
In the CBR system and IR recall and precision are mainly used to measure the retrieval process
of the prototype performance [10].
After identifying the valid cases from the knowledge base as shown in Table 2, the succeeding
phase is calculating the values of recall and precision. In CBR, there are no standard criteria for
the degree of similarity used to identify similar cases from the knowledge base. Different CBR
researchers use different case similarity thresholds.
Even while several researchers proposed no fixed threshold/range, the majority of them
utilized a number between one and zero-point eight, i.e., [1, 0.8]. This means that the number
of instances and the case base’s capacity to deliver an acceptable solution between a maximum
similarity criterion of 100% and a minimum similarity threshold of 80% is between 1 and 0.8 case
similarity-values [9, 10]. The following generic formula is used in the computational equation
to compute precision and recall.
number of relevant cases retrived
Precision = (3)
total number of relevant retrived cases
number of relevant cases retrived
Precision = (4)
total number of relevant retrived cases
The system performance in terms of precision and recall are presented in Table 2. Table 2
shows the precision and recall value in each test case. As shown in Table 2, for each test case
more than average is recorded in both recall and precision. The average recorded in terms of
precision compared with the average recall is marginally lower. That is because of the swap
between precision and recall. To calculate the recall and precision, for example, the first test
case that is case 33 has 4 best relevant cases which are retrieved from 5 relevant cases as shown
in Table 3.
So, to calculate the precision and recall, we follow the above general formula, for instance,
to calculate the precision value: precision is equal to the number of relevant cases retrieved
divided by the total number of cases retrieved. Based on this formula we got the precision
value of 0.66 which is 66% of from 100%. The remaining test cases of their precision values
are calculated by this method. Also, the recall of each test case can be calculated as precision,
for example, to calculate recall value for test case 33, recall is equal to several relevant cases
retrieved divided by several relevant cases in the case base so, we got the recall value of 0.8
which is 80% of from 100%.
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Table 2
Description of the selected case attribute in JCOLIBR.
Test cases Relevant Relevant re- Irrelevant Relevant not Total cases Precision Recall
cases in the trieved Retrieve retrieved retrieved
case base
Case 1 5 5 1 0 6 0.83 1
Case 2 9 7 1 1 8 0.87 0.77
Case 5 10 9 2 1 11 0.81 0.9
Case 7 12 10 1 2 11 0.9 0.83
Case 12 8 6 1 1 7 0.85 0.75
Case 19 6 4 0 2 4 1 0.66
Case 23 9 7 2 1 9 0.77 0.77
Case 21 8 5 3 3 8 0.62 0.62
Case 31 7 6 1 3 7 0.85 0.85
Case 33 5 4 2 1 6 0.66 0.8
Case 40 8 6 1 2 7 0.85 0.75
Case 43 3 2 0 1 2 1 0.66
Case 60 7 7 0 0 7 1 1
Case 77 9 8 2 2 10 0.8 0.88
Average 0.71 0.80
Table 3
Comparison of system performance with previous CBR systems
Disease/Author Used tool System performance measurement
User acceptance Precision Recall
Proposed JCOLIBRI 90% 71% 80%
Hypertension [13] Python 38.2% 60% 86.1%
Triage treatment [14] JCOLIBRI 86.4% 61% 85%
Mental health [15] JCOLIBRI 41.6% 71% 82%
Aids [5] JCOLIBRI 36.9% 63% 72%
Obesity [3] Prolog Not evaluated
6.3. Discussion of the results
The performance of the prototype system is evaluated in different testing techniques. The
first evaluation technique is user acceptance testing. The system is tested by six selected
evaluators. The domain evaluators accepted the system validity with a total average performance
result of 90%. This result indicates that the prototype system is more applicable in terms of
system ease of use, system efficiency in time, user interface interactivity, system accuracy in
categorizing patients correctly, system effectiveness in terms of time and cost for the patient,
system applicability to the medical domain, and system speed. The other technique is system
performance testing with case retrieval. The testing is performed in terms of recall and precision
with fourteen test cases. The average system performance results for precision and recall are
71% and 80% respectively. It is critical to compare the prototype system’s aforementioned
performance findings with past relevant CBR systems in the medical domain area. This is seen
in Table 3. Fig. 8 depicts the link between accuracy and recall.
In Table 3, all authors except [1] attempted to evaluate the performance of the prototype
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Figure 8: Relationship between precision and recall
system. This needs an improvement to increase the system performance. This work achieved
90% which is better compared to the other works in terms of user acceptance testing. Because
in this study no one is evaluated the system as poor and fair, this makes a better improvement
to achieve high value. Generally, the value of system performance measurement i.e. precision
and recall are not equal as compared to the previous research. Because the test case and total
numbers of cases used in the system are different in each previous research. In addition to
the test cases and the total number of cases, the precision and recall value depends on the
threshold interval taken in each research. When a larger threshold interval similarity is applied,
it is feasible to get the most similar instances from the case base. This may result in lower
average recall and greater average accuracy of prototype system performance. The greater the
recall value, the more important things are gathered from the total number of important items
recovered and not retrieved by the system.
7. Conclusion and future work
7.1. Conclusion
Diagnosis and treatment in the medical domain are common tasks. Since this task is very
tedious and requires many health physicians to diagnose and treat the patients, the researcher
develops a knowledge-based system (KBS) for obese patients. A KBS is a good technology
in artificial intelligence to minimize human efforts and to keep the information accurate that
occurred from physicians’ errors. The system is developed using a CBR approach. CBR is
a generic AI paradigm for reasoning from experience, and its technique has been studied in
enhancing human decision-making and in designing KBSs in medicine. The main advantage of
the CBR system in medicine is the automatic formation of a facility-adapted knowledge base
which is a very important aspect in medical decision making.
The primary goal of this research was to create a KBS for the diagnosis and treatment of
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obesity. A dataset was obtained from the University of Gondar Referral Hospital using a semi-
structured interview approach in order to construct a model for the system. The authors use a
decision tree to model the collected knowledge. To develop the prototype system, JCOLIBRI
software was used. The performance of the prototype was evaluated using user acceptance
testing by six domain experts and system performance in terms of precision and recall. The
prototype system concerning user acceptance evaluation achieved 90%. Also, the prototype
system achieved 71% and 80% in terms of precision and recall, respectively.
7.2. Future work
Even though the results of this study are essential, there are still various problems that future
researchers can solve to increase the performance of the system prototype in a real situation.
This will help to assist the medical doctors by adapting the previous cases that have been
retrieved from the case base. For this study, we recommend the following areas as the direction
for future work.
• To achieve high performance, researcher can apply CBR with other AI techniques like
rule base, neural network, fuzzy logic, and other techniques.
• In most CBRS, the knowledge acquisition method is done manually, that is either by
interview or questionnaire. We hope that the other researchers can apply automatic case
elicitation techniques to save time.
• All knowledge-based systems developed with the CBR approach are done in English. To
some extent, this may have some impact on the users of the system. So, developing the
system in the local language, like in Afan-Oromo and Amharic is will be a better work.
• For case retrieval, most CBR researchers use the two basic techniques independently.
These techniques are inductive indexing and the nearest-neighbor algorithm. So, we
recommended using the two basic techniques for future CBR applications.
• The developed KBS on CBR approach only supports cases in text format. So, for future
work, a KBS that supports all cases in any format like image, graphics, and texts as well
can be developed.
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