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The Use of Certainty Factor with Multiple Rules for Diagnosing Internal Disease


International Journal of Application or Innovation in Engineering & Management (IJAIEM), Web Site: Email:,, Volume 1, Issue 1, September 2012, ISSN 2319 - 4847

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									International Journal of Application or Innovation in Engineering & Management (IJAIEM)
       Web Site: Email:,
Volume 1, Issue 1, September 2012                                       ISSN 2319 - 4847

         The Use of Certainty Factor with Multiple
           Rules for Diagnosing Internal Disease
                                    Tb. Ai Munandar1, Suherman2 and Sumiati3
                                       Informatics Eng. Department, Universitas Serang Raya
                                    Jl. KH. Amin Jasuta No. 15 C Serang Banten, Indonesia

Availability of specialists in internal medicine to serve the wider community, especially in the province of Banten is still very
low, so as to obtain health care, patients have to queue up at a particular hospital. Expert systems as a branch of artificial
intelligence course can be used as a solution to overcome the shortage of specialists in the field of medicine. In this study, we
developed an application expert system for diagnosing the disease using inference techniques certainty factor with multiple
premise rule, which allows a patient consultations to determine many of the symptoms, in order to obtain the final diagnosis
according to the patient's level of conviction entered. Application was developed using the concept of web-based so it can be
used at any time by the patient, to be able to detect early symptoms of the disease, prior to further examination by a specialist.
Search results for the selected symptoms of the patient showing the value of certainty factor, and shows the percentage of
convictions diagnosis performed at 90%.

Keywords: Internal disease, expert systems, certainty factor, multiple premises

   An expert system is a branch of artificial intelligence was first developed in mid-1960. As an application of the
scientific study of artificial intelligence, expert systems are able to use human knowledge through an inference engine
to solve problems that require a person expertise. Because of its ability that can capture knowledge as well as ease of
access from anywhere, even able to operate within the dangerous, expert systems are developed for a variety of purposes
from all disciplines [1],[2].
   In general, the application of expert systems are widely used in the health sector to make the diagnosis of certain
diseases, such as HIV disease diagnosis expert system using Fuzzy C-Means [3], Hypotension diagnosis using the
concept of Neuro-Fuzzy [4], low back pain in computer users diagnosis using fuzzy sets [5],[6], diabetes disease
diagnosis using Mamdani fuzzy inference with fuzzy verdict mechanism [7], and diagnosis of asthma using machine
learning methods [8].
   In other areas, an expert system was developed to predict the customer satisfaction with a product based on design
and color [9], predict tensile behavior of tailor welded blanks using artificial neural network [10], development and
improvement of learning concepts using expert system application to create e-learning expert system [11] and mobile
learning are combined with the concept of cloud computing [12], application development 'guru' spiritual uses the
concept of an expert system to answer various questions about life and soul [13], and classification of types of industrial
and home wastes using if-then-rule-based [14].
   On the other hand, the use of expert system inference engine aims to form a final conclusion on the basis of
knowledge. Some inference engines that can be used are fuzzy logic, neural networks, neuro-fuzzy concept if-then rule
based, Bayesians reasoning, genetic algorithms, and even the certainty factor. For the use of certainty factor, at the
beginning of its development, much criticized by scientists because they do not fit and are only used for specific needs
and could not be used, however, now, the certainty factor began to be used again as an inference engine for its ability
who is capable of forming an independent causal assumption [15] and able to handle the appearance of single or
multiple rules to the resulting final conclusion [1].
   Some expert system application, which uses the inference engine certainty factor among others: the level of water
quality monitoring applications [16], similarity of facial expression detection for multimedia applications [17],
evaluation of the success rate of teaching system [18], handling of duplicate data on the concept of data mining [19]
and safety risk assessment process of enterprise information technology adoption [20].

Volume 1, Issue 1, September 2012                                                                                      Page 58
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
       Web Site: Email:,
Volume 1, Issue 1, September 2012                                       ISSN 2319 - 4847

   However, some applications are still developed using a combination of if-then rules and inference certainty factor to
handle double the premise of connecting conjunctions, where inferences are affected in such patients answer "Yes" or
"No" when choosing the symptoms are felt (for if-then-rules), and final value certainty factor combinations, less than
optimal because it only take the lowest or the highest value of evidence, and then multiplied by the value of certainty
factor rule in the existing knowledge base, so that the final value of the degree of confidence is determined by the size
of the value of direct evidence (for certainty factor using connecting conjunctions).
   This paper will discuss the use of the combined value of certainty factor of multiple rule without seeing the lowest or
the highest value, so that the final value is more optimal results without relying on any of the evidence that exists,
because all the evidence has the same potential to be able to determine the final decision.

   2.1 Expert System
   In the development of expert systems, basically there are several important components that must exist, namely,
expertise, experts, transfer of expertise, inference, and the ability to explain the rules of the facts, theories, procedures,
and rules of the problem in order to resolve problems [2], [21]. An expert system consists of two main parts (see Figure
1). First, the development environment, which is used by the manufacturer, to build an expert system components, and
introduce knowledge into the knowledge base. Secondly, the environmental consultancy, which is used by the user, in
order to acquire the knowledge and advice of the expert system, as if it were an actual consultation with experts.
Inference engine are the most widely used, is a forward chaining and backward chaining, or a combination of both,
however, they are sometimes combined with other methods to be more stable inference engine.

                                           Figure 1 Expert System Environment

   2.2 Internal Disease
   Internal disease is kind of a disease that attacks the human internal organs. In medicine, known for some of the
common diseases suffered by the patient, such as, liver disease, heart disease, pulmonary tuberculosis, gout, coronary
heart disease, anemia, brain abscess, etc [22]. This research will discuss the ten types of internal disease, to include
disease information, symptoms and rules into the knowledge base of expert system applications.
   Each disease has several symptoms different or even the same. In this study, data on symptoms were found in each of
these diseases, there is a common symptom of some other disease. This is what underlies the certainty factor used as a
method of inference engine to determine the final decision will be.
   For example, the symptoms with code GJ013, (headaches) are not only found in the brain abscess symptoms, but also
felt by people with anemia disease. Likewise with code GJ012 (a fever), is not only felt by people with brain abscess
disease but also experienced by kidney disease, or other diseases.

Volume 1, Issue 1, September 2012                                                                                  Page 59
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
       Web Site: Email:,
Volume 1, Issue 1, September 2012                                       ISSN 2319 - 4847

  2.3 Certainty Factor (CF)
  The concept of certainty factor is proposed to accommodate the uncertainty of an expert who is often thought of
analyzing information with phrases such as "May", "likely", "almost certainly" and so on. Notation certainty factor is
described as follows:

                                                CF[h,e]=M[h,e] – MD[h,e] (1)

   Where, CF [h, e] depict the certainty factor and MB [h, e] describes the size of the confidence in the hypothesis h, if
given evidence e (between 0 and 1), MD [h, e] describes the size of the distrust of hypothesis h, if given evidence e
(between 0 and 1). There are three things that might happen to the hypotheses arising from existing evidence. First, the
rules with single evidence e and a single hypothesis of h, where the notation certainty factor can be written as follows:

                                              CF[h,e]=CF(e) x CF(rule) (2)

   In practice, CF (rule) is determined by experts, whereas CF (e) is determined by the user, while consulting with the
system. Second, rules with multi- evidence e and single hypothesis h. Notation certainty factor is determined by the
interface used, could use a disjunction or conjunction. Notation on connective disjunction is written as follows:

                                      IF e1 AND e2 ... AND en THEN h (CF rule) (3)

  So that the calculation of the value of CF combination, defined by the following notation:

                                      CF(h,e)=min[CF(e1),CF(e2),...,CF(en)]*CF(rule) (4)
  Meanwhile, for connecting conjunction, can be written as follows:

                                        IF e1 OR e2 ... OR en THEN h (CF rule) (5)

  So the calculation of the value of CF combination, defined by the following notation:

                                  CF[h,e] = max[CF(e1),CF(e2),...,CF(en)]*CF(rule)   (6)

  Third, the combination of two rules with different evidence (e1 and e2) but refers to the same hypothesis. The
equations for combining two CF are as follows:


  In this study, the combination formulation CF calculations, using the formula 7.a, to calculate the value of CF dual
premise, while for a single premise, we use the formula 4, the terms entered by the user CF ranges between (0 and 1).

   This study is divided into several steps of research, beginning with the stage of data collection and information
symptoms of each disease. Once the symptoms of the information obtained, and then classified according to the type of
disease. If there are some of the same symptoms for different diseases, the same symptoms are put together and then
made a symptom code to be included in the knowledge base and are related to the type of illness. The next stage is the
development of a knowledge base using MYSQL database engine and manufacturing expert system application. The
next stage is the development of a knowledge base using MYSQL database engine and manufacturing expert system
application, and then tested against some users to test the accuracy of the final result.

  Applications developed, designed by interactive condition, in which the user is not exposed to the type of question
that requires an answer Yes or No. In the developed application, the user as if faced with a real doctor, with
consultation on the page, the system will ask about body parts that feel certain symptoms. For example, if the user typed

Volume 1, Issue 1, September 2012                                                                               Page 60
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
       Web Site: Email:,
Volume 1, Issue 1, September 2012                                       ISSN 2319 - 4847

the body "head", it will display some kind of symptoms that can be selected by the user, as well as other body parts. If
the user has selected some of the symptoms, then the user is faced with the consultation page to give CF according to
their beliefs. This CF value will be calculated according to the value of CF that exists in the knowledge base(see figure

                                   Figure 2 Flowchart Expert System Consulting Process

   Selection of the symptoms can be done repeatedly during the user believes that there are many symptoms felt. If users
already feel confident enough, then the user can simply perform a search end result. The system will make the process
of inference to symptoms of CF are chosen with the value entered. For example, a user consultation as follows:

                                         Table 1: Data of the selected user's symptoms

    No      Symptoms and CF value from user
    1       Feeling short of breath with CF value of 0.70
    2       Chest tightness and spasms, making breathing so breathless with CF value of 0.60
    3       Often feel uncomfortable, as if not enough air into the lungs, such as climbing stairs with CF value of 0.80
    4       It felt as though the narrowing of the chest, as bound with rope, with a value of 0.60 CF
    5       Neck stiff / sore CF value of 0.70
    6       If rheumatic attack left hand, shooting pain was radiating to the shoulders to the neck with a CF value of 0.50
    7       Frequent headaches with CF value of 0.60
    8       Feeling weak and / or dizziness, or feeling very weak to get out of bed with a CF value of 0.40

  Based on the symptoms are selected, the system detects some types of diseases that meet these symptoms. For
example, the symptoms 5 (CFg5 = 0.70) and symptoms 7 (CFg7 = 0.60), suggesting a brain abscess symptoms with
each CF rule (CFR1 = 0.80) and (CFR2 = 0.70), so the value of CF combination is obtained as follows:

                                           CF1 = CFg5 x CFR1 = 0.70 x 0.80 = 0.56
                                           CF2 = CFg7 x CFR1 = 0.60 x 0.70 = 0.42
  Since both values CF (CF1 and CF2) > 0, then we use the equation of 7.a, so its value is obtained as follows:

                                             CFcomb = CF1 + CF2(1 – CF1)
                                                  = 0.56 + 0.42(1 – 0.56)
                                                  = 0.74
   In the same conditions, symptoms 7 (CFg7 = 0.60) appears on the symptoms of anemia with CF rule (CFR1 = 0.60),
in addition to seven symptoms, other symptoms that appear when the user selects the initial symptom is a symptom 1
(CFg1 = 0.70) with a CF rule of the knowledge base of (CFR2 = 0.50) and symptoms 8 (CFg8 = 0.40) with a CF rule
(CFR3 = 0.60). Thus, the combinations of CF values obtained are as follows:

Volume 1, Issue 1, September 2012                                                                                          Page 61
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
       Web Site: Email:,
Volume 1, Issue 1, September 2012                                       ISSN 2319 - 4847
                                             CF1 = CFg1 x CFR2 = 0.70 x 0.50 = 0.35
                                             CF2 = CFg7 x CFR1 = 0.60 x 0.60 = 0.36
                                             CF3 = CFg8 x CFR3 = 0.40 x 0.60 = 0.24
                                             CFcomb = CF1 + CF2(1 – CF1)
                                               = 0.35 + 0.36(1 – 0.35)
                                               = 0.58
                                             CFcomNew = CFcomb + CF3(1 – CFcomb)
                                               = 0.58 + 0.24(1 – 0.58)
                                               = 0.68

   Overall, the value of CF following a search result, for all the symptoms that have been selected, based on the
calculation expert system (see table 2)
   Based on the eight symptoms experienced by the user during a consultation, the relationship is likely some type of
disease suffered by the user is shown by Table 2. Highest CF value indicates that the type of disease is exactly what the
truth is closer to the level of the best of search results the user perceived symptoms.
   The number of emerging diagnostic results (see table 2) shows that the use of certainty factor, able to provide better
decision than just using regular inference methods. It shows also that, any of your symptoms are associated with other
diseases, and it requires the correct inference techniques to prevent a single diagnosis. With the advent of multiple
diagnoses, a patient is able to know the type of the disease according to the degree of accuracy available

                              Table 2: CF combination value of expert systems search results
                                   Disease                    CF value     Accuracy(%)
                                   Heart Failure              0.909        90.8608%
                                   Brain abscess              0.735        73.48%
                                   Anemia                     0.684        68.384%
                                   Bronchial asthma           0.629        62.88%
                                   Coronary Heart             0.490        49%
                                   Pulmonary tuberculosis     0.490        49%
                                   Hyperthyroid               0.360        36%

   Expert system search results as shown in Table 2 indicate that any symptoms are selected by the user will be
searching for all kinds of diseases that meet the symptoms. If found congruence, between the symptoms of the disease,
then the system will calculate the value of CF combination in accordance with existing rules to the knowledge base and
CF values entered by the user. The results displayed will be different because it depends on the selected symptoms.
Highest confidence level will determine the best final decision; following a lower level of confidence in the final
decision is the alternative to choose. Shape the final recommendations can be made up of more than two types of the
disease, depending on the selection of user perceived symptoms.
   The use of methods of certainty factor in expert systems, not only provide a single diagnosis, however, several other
diagnostic possibilities associated with the symptoms experienced will be raised in accordance with the results of the
calculation CF combination. Confidence level of 90% of search results..

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               Tb. Ai Munandar received the B.S. and M.S. degrees in Informatics Engineering from Universitas
               PGRI Yogyakarta (B.S) in 2008 and Universitas Atma Jaya Yogyakarta (M.S) in 2012. During 2008-
               2009, he stayed in Index-Forex Trading Company as a Network Engineering, in the midle of 2009-
               2011, he become a teacher at some computer science senior high school and some private universities.
               Now, he is working as a lecturer in Universitas Serang Raya. His research interests are soft computing
               techniques, digital image processing, e-business, project management and data mining

Volume 1, Issue 1, September 2012                                                                          Page 63
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
       Web Site: Email:,
Volume 1, Issue 1, September 2012                                       ISSN 2319 - 4847

               Suherman received the B.S and M.S degrees in Informatics Engineering from Sekolah Tinggi Sains
               dan Teknologi Indonesia (STT-INTEN) (B.S) in 2000 and STT Benarif (M.S) in 2010. During 2005 –
               now, he stayed in Universitas Serang Raya as a lecture, secretary of dean in Information Tech.
               Department, and Dean of Information Tech. Department (Now). His reseach interests are, artificial
               intelligence, digital image processing, software engiinering and enterprise information systems.

Sumiati received the B.S degree in Informatics Engineering and M.S in Economic Field from Universitas Persada
Indonesia – YAI in 2004 and 2008, respectively. During 2005 – 2009, she stayed in one of national private bank as an
administration staff. During 2009 – now, she become a lecture at Universitas Serang Raya. Her research interests are
artifial intelligence, digital image processing, software engineering and data mining.

Volume 1, Issue 1, September 2012                                                                         Page 64

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