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International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com December 2016, Volume 4, Issue 12, ISSN 2349-4476
Rule Based Expert System for Medical Diagnosis-A Review
Divya Mishra
College of Engineering Roorkee,
Deepak Painuli
Quantum School of Management
Abstract— Traditionally human experts were responsible
for taking decisions in solving the medical problems. But
it was very difficult for human expert to solve complex
problems,for that expert systems were designed.There
are lot of applications in artificial intelligence domain
that try to help human experts offering solutions for a
problem.
Expert system is a part of artificial intelligence which
increases the ability of decision making of the human
expert. They are designed to solve complex
problems.Rule based expert system uses rules as the
knowledge representation for knowledge coded into the
system. Rule based expert system is used by the human
experts to diagnose the
problem.This paper surveys research work accomplished
in the field of medical sector using rule based expert
system.
Keywords: Expert System, Rules, Knowledge,
Diagnosis, Diseases, Medical Problems
I. INTRODUCTION
Expert systems use artificial intelligence to model a
decision that experts in the field would make.
Unlike decision support systems that provide
several options from which the user may choose,
expert systems convey the concept that the
computer has made the best decision based upon
criteria that experts would use.”
(Hebda, Czar & Mascara, 1998, p. 254).
Expert systems generate decisions that an expert
would make: they can recommend solutions to
nursing problems which mimic the clinical
judgement of a nurse expert. These systems are
developed to facilitate and enhance the clinical
judgement of nurses, not to replace them. Like
decision support systems, expert systems provide
information to help health professionals to make
informed judgements when assessing the validity of
data, information, diagnoses, and choices for
treatment and care.
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Divya Mishra, Deepak Painuli , Nirvikar
Nirvikar
College of Engineering Roorkee
Expert systems are comprised of four main
components:
a) a natural language (such as English) to interface
and interact with the user.
b) a knowledge base containing the rules from
which the decisions can be made.
c) a database of facts specific to the domain of
focus.
d) an inference engine to solve problems by linking
the knowledge base rules with the database, using
heuristics or “rules of thumb” logic.
`The objective of this paper is to survey the research
work done in the field of medical through rule
based expert system.
II. WHAT ARE RULE-BASED SYSTEMS?
Conventional problem-solving computer programs
make use of well-structured algorithms, data
structures, and crisp reasoning strategies to find
solutions. For the difficult problems with which
expert systems are concerned, it may be more useful
to employ heuristics: strategies that often lead to the
correct solution, but that also sometimes fail.
Conventional rule-based expert systems, use human
expert knowledge to solve real-world problems that
normally would require human intelligence. Expert
knowledge is often represented in the form of rules
or as data within the computer.
Depending upon the problem requirement, these
rules and data can be recalled to solve problems.
Rule-based expert systems have played an
important role in modern intelligent systems and
their applications in strategic goal setting, planning,
design, scheduling, fault monitoring, diagnosis and
soon.
With the technological advances made in the last
decade, today‟s users can choose from dozens of
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com December 2016, Volume 4, Issue 12, ISSN 2349-4476
commercial software packages having friendly
graphic
user
interfaces
(Ignizio,
1991).
Conventional computer programs perform tasks
using a decision-making logic containing very little
knowledge other than the basic algorithm for
solving that specific problem. The basic knowledge
is often embedded as part of the programming code,
so that as the knowledge changes, the program has
to be rebuilt. Knowledge-based expert systems
collect the small fragments of human know-how
into a knowledge base, which is used to reason
through a problem, using the knowledge that is
appropriate. An important advantage here is that
within the domain of the knowledge base, a
different problem can be solved using the same
program without reprogramming efforts.
Moreover, expert systems could explain the
reasoning process and handle levels of confidence
and uncertainty,which conventional algorithms do
not handle (Giarratano and Riley, 1989). Some of
the important advantages of expert systems are as
follows:
• ability to capture and preserve irreplaceable
human experience;
• ability to develop a system more consistent than
human experts;
• minimize human expertise needed at a number
of locations at the same time (especially in a
hostile environment that is dangerous to human
health);
• solutions can be developed faster than human
experts.
The basic components of an expert system are
illustrated in Figure 1. The knowledge base stores
all relevant information, data, rules, cases, and
relationships used by the expert system. A
knowledge base can combine the knowledge of
multiple human experts. A rule is a conditional
statement that links given conditions to actions or
outcomes. A frame is another approach used to
capture and store knowledge in a knowledge base. It
relates an object or item to various facts or values.
A frame-based representation is ideally suited for
object-oriented programming techniques. Expert
systems making use of frames to store knowledge
are also called frame-based expert systems
.
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Divya Mishra, Deepak Painuli , Nirvikar
The purpose of the inference engine is to seek
information and relationships from the knowledge
base and to provide answers, predictions, and
suggestions in the way a human expert would. The
inference engine must find the right facts,
interpretations, and rules and assemble them
correctly. Two types of inference methods are
commonly used – Backward chaining is the process
of starting with conclusions and working backward
to the supporting facts. Forward chaining starts with
the facts and works forward to the conclusions.
INFERENCE ENGINE IN RULE-BASED
SYSTEMS
A rule-based system consists of if-then rules, a
bunch of facts ,and an interpreter controlling the
application of the rules, given the facts.
These if-then rule statements are used to formulate
the conditional statements that comprise the
complete knowledge base. A single if-then rule
assumes the form „if x is A then y is B ‟ and the ifpart of the rule „ x is A ‟is called the antecedent or
premise ,while the then-part of the rule „ y is B ‟ is
called the consequent or conclusion .There are two
broad kinds of inference engines used in rule-based
systems: forward chaining and backward chaining
systems.
In a forward chaining system, the initial facts are
processed first, and keep using the rules to draw
new conclusions given those facts. In a backward
chaining system, the hypothesis (or solution/goal)
we are trying to reach is processed first, and keep
looking for rules that would allow to conclude that
hypothesis. As the processing progresses, new
subgoals are also set for validation. Forward
chaining systems are primarily data-driven, while
backward chaining systems are goal-driven.
Consider an example with the following set of ifthen rules
Rule 1
:If A and C then Y
Rule 2
:If A and X then Z
Rule 3
:If B then X
Rule 4
:If Z then D
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www.ijetmas.com December 2016, Volume 4, Issue 12, ISSN 2349-4476
If the task is to prove that D is true, given A and B
are true.
According to forward chaining , start with Rule 1
and go on downward till a rule that fires is found.
Rule 3 is the only one that fires in the first iteration.
After the first iteration, it can be concluded that A,
B ,and X are true. The second iteration uses this
valuable information. After the second iteration,
Rule 2 fires adding Z is true, which in turn helps
Rule 4 to fire, proving that D is true. Forward
chaining strategy is especially appropriate in
situations where data are expensive to collect, but
few in quantity. However ,special care is to be taken
when these rules are constructed, with the
preconditions specifying as precisely as possible
when different rules should fire.
In the backward chaining method, processing starts
with the desired goal, and then attempts to find
evidence for proving the goal. Returning to the
same example, the task to prove that D is true
would be initiated by first finding a rule that proves
D. Rule 4 does so, which also provides a subgoal to
prove that Z is true. Now Rule 2 comes into play,
and as it is already known that A is true, the new
subgoal is to show that X is true. Rule 3 provides
the next subgoal of proving that B is true. But that B
is true is one of the given assertions. Therefore, it
could be concluded that X is true, which implies
that Z is true, which in turn also implies that D is
true. Backward chaining is useful in situations
where the quantity of data is potentially very large
and where some specific characteristic of the
system under consideration is of interest. If there is
not much knowledge what the conclusion might be,
or there is some specific hypothesis to test, forward
chaining systems may be inefficient. In principle,
we can use the same set of rules for both forward
and backward chaining. In the case of backward
chaining, since the main concern is with matching
the conclusion of a rule against some goal that is to
be proved, the „then‟ (consequent) part of the rule is
usually not expressed as an action to take but
merely as a state, which will be true if the
antecedent part(s) are true (Donald, 1986).
III. Literature Survey
“A Medical Expert system in cardiological
diseases” by E.Bursuk, M.Ozkan and B. Ilerigelen
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Divya Mishra, Deepak Painuli , Nirvikar
IEEE[1999].The author establish a medical expert
system for diagnosis cardiological diseases. If-Then
Rules were used as the methodology. The data of 25
cardiological patients were used and out of 3
evaluation methods: True Positive, False Negative
and False Positive ,the result of this program is True
Positive.
“Expert System for early diagnosis of eye diseases
infecting the Malaysian population” by Fatimah
Ibrahim, Juliana Basheer Ali, Fathilah Jaais and
Mohd Nasir Taib IEEE,[2001].The author describes
knowledge based system to detect different eye
diseases found in Malaysia. Five types of eye
diseases can be detected by this system namely
senile,secondary,open
angle,acute
and
allergic.Commercial expert system shell software
known as EXSYS Professional. It is a rule based
expert system development tool running on IBM
compatible pc The system was tested successfully
but only two patients consult this system due to
some constraints.
Approach to combining case based reasoning with
rule based reasoning for lung disease diagnosis” by
Nguyen Hoang Phuong, Nadipuram R. Prasad,
Dang Huu Hung and Jeffery T.Drake
IEEE[2001].In this paper the author proposed an
approach to combine case base and rule base to
diagnose lung disease. Case based reasoning with
three components:knowledge base(18 lung disease),
inference engine and explanation mechanism and
rule based reasoning with If-Then rules. A method
combining fuzzy set theory for case based reasoning
and rule based reasoning for lung disease is
proposed
Expert system methodologies and application-a
decade review from 1995 to 2004” by Shu-Hsien
LiaoElsevier,2005. This paper surveys expert
system methodologies from 1995 to 2004. Eleven
categories
are
surveyed:
rule-based
system,knowledge based system,fuzzy Ess,object
oriented methodology, case based reasoning, system
architecture, intelligent agent systems,database
methodology,modelling
and
ontology.ES
methodologies are tending to develop towards
expertise orientation and that ES applications
development is a problem oriented domain.
A rule based disease diagnostic system using a
temporal relationship model” by Chien-Chin
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Wang,Ming-Nan Chien, Chua-Huang Huang and Li
LiuIEEE,2007. A temporal model is used to express
relationship among diseases. The model defines six
types of relationship: before, after, contained,
contains, start_overlap and end_overlap. This model
is implemented as a rule based system using Java
based expert system,JESS. The purpose of this
paper is to provide a system kernel for applications
of medical diagnosis system. The result is a
collection of temporal relationships of two diseases.
An expert system for diagnosis eye diseases using
CLIPS” by Samy S.Abu Naser and Abu Zaiter
A.Ola in 2008. An expert system is designed to
provide the patient to suitably diagnose some of the
eye diseases.CLIPS is used as a tool. This system
help doctors and patients in providing decision
support system, interactive training tool and expert
advice.
“A review of knowledge based systems in medical
diagnosis” by Mrs. S.S Gulavani and R.V Kulkarni
in 2009. This paper includes basic research done in
the field of medical sector. This paper includes
basic research done in the field of medical
sector.Author conclude that knowledge base system
is extensively used in the medical diagnosis
An expert system for diagnosis human diseases” by
P.Santosh Kumar Patra, Dipti Prava Sahu and
Indrajit Mandal in 2010. Author proposed a
diagnosis expert system to deal with different
human diseases. DExS,Diagnosis expert system was
used as the methodology.Works only on few
diseases and several properties remain to be
investigated.
“Architecture for medical diagnosis using rulebased techniques” by Prem Pal Singh Tomar and P
K Saxena in 2011.A rule based diagnostic decision
support system was proposed by the author to
identify diseases and describing methods of
treatment to be carried out .Rule based system
model .The results of rule based reasoning strategy
indicated the retrieval accuracy of 100% with
correct symptoms.
“Rule based expert system for diagnosis of
neuromuscular disorders” by Rajdeep Borgohain
and Sugata Sanyal in 2012.The implementation of a
rule based system for diagnosing neuromuscular
diseases is proposed by the author.JESS is used for
diagnosis
multiple
sclerosis,
cerebral
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Divya Mishra, Deepak Painuli , Nirvikar
palsy,muscural
dystrophy
and
Parkinson‟s
disease.After testing against some proven cases of
neuromuscular disorders ,the system shows accurate
results.
“Rule based expert system for cerebral palsy
diagnosis” by Rajdeep Borgohain and Sugata
Sanyal in 2012 .A rule based expert system was
proposed by the author to identify cerebral palsy
disease and also describe whether the disease is
mild,moderate or severe. Rule based expert system
through JESS. After testing against some proven
cases of cerebral palsy,the system shows accurate
results.
“Rule based expert system for diagnosis of fever”
by Sunday Tunmibi, Oriyomi Adenji, Ayooluwa
and Ayodeji Dasylva in 2013. The implementation
of a rule based system for diagnosing fever is
proposed by the author.The system deals with the
patients in English written rules If/Then rules and
some codes were also written in VB.Net for making
deduction of new facts from rules in knowledge
base. E-diagnosis system was proposed.This paper
designs a rule based system for diagnosis of
feversuchasmalaria,typhoid,pelebstein,leptospirosis,
scarlet,denguerheumatic,hey and lassa fever .
“An Adoptive Medical Diagnosis System using
expert system with applications” by Gufran Ahmed
Ansariin 2013. An adoptive medical diagnosis
system using expert system was proposed by the
author that helps the patients infected with some
common diseases and prescribe a medical
support.Adoptive medical diagnosis system was
used. This is useful to diagnose patient disease and
prescribe the good prescription to the patients as a
human expert .This AMDS system is applied to any
hospital ,any country for improving medical
services.
“Rule based expert system for viral infection
diagnosis” by Maitri Patel, Atul Patel and Paresh
Virparia in 2013 .Web based system for diagnosing
viral infection was
proposed by the author.
Medications to these infections can be deduced
through the system.The system is deployed on the
web server. The web server is a dual server
comprising of a Tomcat container used for request
response cycle processing and MSQL for
knowledge baseThis system is beneficial for the
medicine practitioners for not so serious viral
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www.ijetmas.com December 2016, Volume 4, Issue 12, ISSN 2349-4476
infection diagnosis.
“Fuzzy rule based expert system for diagnosis of
multiple sclerosis” by M.Arabzadeh Ghahazi,
M.H.Harirchian, M.H.Fazel Zarandi, S.Rahimi
Damirchi-Darasi in 2014 .This paper presents a
fuzzy rule based expert system for MS diagnosis.
This system can help to non-neurologists in the
diagnosis of MS or can be used as a neurologist
physician assistant.The system uses a spreadsheet
for storing or extracting information of the
patientsMagnetic Resonance Imaging has a decisive
role in diagnosis of MS.A diagnostic system is
helpful for diagnosing the problem. Crisp values
were used as binary values that cause the results in
the physician‟s mind that associated with
uncertainty and vagueness.Using person‟s identity,
symptoms and clinical observations and applying
uncertainty in diagnosis. The proposed system has
the ability of modelling fuzzy rules with binary
premise and uncertain consequent.
“Rule based expert system for the diagnosis of
memory loss diseases” by Miss Komal R. Hole and
Prof. Vijay S. Gulhane in 2014. Rule based expert
system for diagnosing memory loss disease was
proposed by the author with the help of rules and
facts. Also case based approach is used for saving
the cases and for comparing the new case with
previously saved cases. This system helps patients
to get the required advice about the different
disorders attack to them due to their nervous system
disorders.Rule based system and case based system
was used. Four diseases are covered which are
Alzheimer Disease, Dementia, Parkinson Disease,
Huntington disease. For Dementia and Alzheimer
disease the primary test is required namely MMSE
(Mini Mental State Examination). Several
properties were left for investigation.
“Rule based expert system for disease diagnosis” by
Adewole K.S, Hamboli M.A and Jimoh M.K in
2015. This paper presents a rule based expert
system for diagnosing malaria, typhoid, cholera,
tuberculosis and breast cancer. This system consist
of 46 rules to diagnose the diseases.The goal of this
research is to replace the manual method of
diagnosing the diseases by medical expert. The rule
based approach uses IF-THEN rulesThe system is
found capable of assisting medical experts in
diagnosing diseases and to provide good health
services to the patients.
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Divya Mishra, Deepak Painuli , Nirvikar
“Type-2 fuzzy rule based expert system for
ankylosing spondylitis diagnosis ” by Maede
Maftouni, M.H. Fazel Zarandi, I.B. Turksen and
Faezeh in 2015. Goal of this research is to design a
type-2 fuzzy rule-based ES for AS diagnosis where
rules are evidence based. Its basic aim is to establish
a narrow set of criteria for diagnosis based on
research studies.Type-2 fuzzy rule based system
with four modules:knowledge base, working
memory, inference engine and graphical user
interface. In this two approaches were used: forward
chaining and backward chainingTwo rule sets were
embedded in the rule base to investigate the primary
suspicion and the concluded diagnosis.
“Study of need and framework of expert system for
medical diagnosis” by Dr.Mamta Baheti in 2016.
This paper provides a path for developing fuzzy
tools for medical diagnosis where there is still a
scope of prediction accuracy. In this paper the
various fuzzy rule based systems along with their
individual features are studied. Mamdani inference
method was used.In rural areas there is a great need
of medical diagnosis expert system. In this paper
the author had mentioned fuzzy ES for soving
complex medical diagnosis.
“The diagnostic value of skin disease diagnosis
expert system” by Fatemeh Rangrez Jeddi, Masoud
Arabfard, Zahra Arabkermany in 2016. The aim of
this study was to determine the diagnostic value of
expert system for diagnosis of complex skin
diseases.A case control study was conducted in
2015 to determine the diagnostic value of an expert
system. Comparing the results of expert system and
physician‟s diagnosis at the evaluation stage
showed an accuracy of 97.1%, sensitivity of 97.5%
and specificity of 96.5%.
IV. CONCLUSION
In this report, a literature survey has been carried
out for the major topics in the area of Expert System
to diagnose diseases. The major issues in this area
of rule based system to diagnose autoimmune
diseases have been identified .I am planning to
work in the direction of rule based system in expert
system that include rules and facts collected from
different experts to diagnose different autoimmune
diseases .
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com December 2016, Volume 4, Issue 12, ISSN 2349-4476
REFERENCES
[1].
[2].
[3].
[4].
[5].
[6].
[7].
[8].
[9].
[10].
[11].
[12].
JamesF.Porter
,Lawrence
C.Kingsland,
Indervadan Shah and James M.Benge 1988.In The
AI/Rheum Knowledge base computer consultant
system in Rheumatology.
Peter Jackson 1998.Introduction to Expert
system,Third Edition.
Hetem,V.2000.Communication: computer aided
engineering in the next millennium, ComputerAided Design, 32, pp.389-394.
Tan, C.F. 2007. An Expert Fault Diagnosis
System for Auto Wire Bond Machine, Journal
Teknologi,47(A), pp.55-73.
Samy S. Abu Naser and Abu Zaiter A. Ola 2008.
An expert system to diagnose eye diseases using
Clips.
S.J.Gath and Dr.R.V. Kulkarni 2012. A Review:
Expert System for Diagnosis of Myocardial
Infarction.
Tan, C.F. and Kher, V.K. 2012. A Fault Diagnosis
System for Industry Pipe Manufacturing Process,
International Review of Mechanical Engineering,
6(6), pp.1292-1296.
Tan, C.F., Kher, V.K. and N. Ismail. 2012. An
Expert Carbide Cutting Tools Selection System
for CNC Lathe Machine, International Review of
Mechanical Engineering, 6(7), pp.1402-1405.
V. Govindan et al,2012. “Using Rule-Based
Reasoning and Object-Oriented Methodologies to
Diagnose Diabetes” Journal of Social Sciences 8
(1): 66-73,2012
P. Santosh Kumar Patra 2012. Automatic
Diagnosis of Diabetes by Expert System.
Rule Based Expert System for Cerebral Palsy
Diagnosis”
by
Rajdeep
Borgohain,2012.Departmentof Computer Science
and Engineering, Dibrugarh University Institute of
Engineering and Technology, Dibrugarh, Assam
and Sugata Sanyal, School of Technology and
Computer Science, Tata Institute of Fundamental
Research, Mumbai, India
Rajdeep Borgohain and Sugata Sanyal 2012.“Rule
Based Expert System for Diagnosis of
172
Divya Mishra, Deepak Painuli , Nirvikar
[13].
[14].
[15].
[16].
[17].
[18].
[19].
[20].
[21].
[22].
Neuromuscular Disorders” Int. J. Advanced
Networking and Applications, Volume:04
Issue:01 Pages:1509-1513 ISSN : 0975-0290,
2012
Tan, C.F., Kher, V.K. and Ismail, N. 2013. Design
of a Feature Recognition System for CAD/CAM
Integration, World Applied Science Journal,
21(8),pp.1162-1166.
Gufran Ahmed Ansari 2013.An adoptive medical
diagnosis system using expert system with
applications
Journal of Emerging Trends in Computing and
Information Sciences Vol. 4, No. 3 Mar 2013,
ISSN 2079-8407
Ibrahim M. Ahmed , Abeer M. Mahmoud
,Mostafa Aref and Abdel Badeeh M. Salem, 2013.
A study on Expert Systems for Diabetic Diagnosis
and Treatment. ISBN: 978-960-474-304-9
Miss Komal R. Hole and Prof. Vijay S. Gulhane,
2014. Rule based expert sytem for diagnosis of
memory loss disease
Jimmy Singla, 2014. Medical Expert Systems for
Diagnosis of Various Diseases T. International
Journal of Computer Applications (0975 – 8887)
Volume 93 – No.7, May 2014
Hannes Alder and Beat A. Michel and Christian
Marx and Giorgio Tamborrini and Thomas
Langenegger and Pius Bruehlmann and Johann
Steurer and Lukas M. Wildi 2014. In ComputerBased
Diagnostic
Expert
Systems
in
Rheumatology: Where Do We Stand in 2014
Autoimmune Disease Guide,2015. Everything
You Need to Know About Autoimmune Disorders
and Autoimmunity by John Sichel.
A.A.L.C. Amarathunga and E.P.W.C. Ellawala,
G.N.Abeysekara and C.R.J. Amalraj 2015. Expert
system for diagnosis skin diseases
Mohammed Abdullah Alghamdi, Sunil G Bhirud,
Afshar M. Alam 2015. Physician's Decision
Process for Disease Diagnosis of Overlapping
Syndrome in Liver Disease using Soft Computing
Model.