Implementing Clinical Decision Support System Using Naïve Bayesian Classifier

International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
Volume: 5 Issue: 12 220 - 225
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IJRITCC | December 2017, Available @ http://www.ijritcc.org
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Implementing Clinical Decision Support System Using Naïve Bayesian
Classifier
Trupti S. Mokati
Dept. of Computer Sci. & Engineering, P.R.Pote (Patil)
College of Engineering and Management, Amravati
tmokati@gmail.com
Prof. Vijay B. Gadicha
HOD Comp. Sci. Dept.,
P.R.Pote College, Amravati
v_gadicha@rediffmail.com
Abstract: To speed up the diagnosis time and improve the diagnosis accuracy in today‟s healthcare system, it is important to provide a much
cheaper and faster way for diagnosis. This system is called as Clinical Decision Support System (CDSS). With various data mining techniques
being applied to assist physicians in diagnosing patient diseases with similar symptoms, has received a great attention now a days. The
advantages of clinical decision support system include not only improving diagnosis accuracy but also reducing diagnosis time. In this paper, the
data mining technique name Naïve Bayesian Classifier, which offers many advantages over the traditional methods of data mining is used that
opens a new way for clinicians to predict patient‟s diseases. As the system is built on the sensitive data for patient privacy it is necessary to add
some features that meets the security requirement. Specifically, with large amounts of data related to healthcare is generated every day, the
classification can be utilized to excavate valuable information that improve clinical decision support system. Here the fuzzywuzzy string
matching algorithm of naïve bayesian classifier is used to perform prediction from large number of symptoms data. The Result analysis perform
in the last section on live data of five patient gives that by using proposed technique we try to make the Clinical Decision Support System more
helpful for providing diagnosis of deceases more accurately and efficiently.
Keywords: Clinical Decision Support System (CDSS), Privacy Preserving, Naïve Bayesian Classifier, Fuzzywuzzy algorithm.
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I. INTRODUCTION
Today‟s Healthcare industry has the global scope to
provide health services for patients. One of the part of it is
Clinical Decision Support System (CDSS) has a massive
amounts of electronic data and experienced such a sharp
required and growth rate. However, it is necessary to design
and develop as appropriate technique to ﬁnd great potential
economic values from large amount of data and to speed up the
diagnosis time and improve the diagnosis accuracy [1]. It is a
new system in healthcare industry that is workable to provide a
much cheaper and faster way for diagnosis. As the Clinical
Decision Support System (CDSS) has huge amount of data it is
necessary to apply various data mining techniques to assist
physicians in diagnosing patient diseases with different data
mining classification functions, and has received a great
attention recently [2] [3] [4]. Out of different data mining
classification techniques available Naive Bayesian classifier, is
one of the popular machine learning tools, has been widely
used to perform prediction [5]. Despite its simplicity, it is more
appropriate for medical diagnosis in healthcare than some
sophisticated techniques [6] [7].
The CDSS with naive Bayesian classifier has offered
many advantages over the traditional healthcare systems and
opens a new way for clinicians to predict patient‟s diseases.
However, one of the main challenges is how to keep patient‟s
medical data away from unauthorized disclosure. The usage of
medical data can be of interest for a large variety of healthcare
stakeholders [8]. Without good protection of patient‟s medical
data, patient may feel afraid that his medical data will be
leaked and abused, and refuse to provide his medical data to
CDSS [9]. Therefore, to develop the clinical decision support
system along with address the privacy issues, this paper
propose a Privacy Preserving Patient-Centric Clinical Decision
Support System, called PPCDSS. For preserving the privacy of
patient‟s medical data, here the sensitive data gets encrypted
first by using cryptographic approach and then stored to the
data base.
Along with this some of the Objectives which are
targeted to achieve are perform efficient prediction of disease
on the basis of existing data-set. For that the system introduce a
new classification and aggregation approach called
fuzzywuzzy, which allows service provider to build naive
Bayesian classifier [10]. This helps in reducing diagnosis time
for prediction of diseases. And the encryption technique used
helps for preserving privacy of patient‟s data. As the symptoms
are vary from patient to patient and may not be present in
CDSS database for that on new mechanism is proposed as to
review and retrain the CDSS dataset [11]. The remaining paper
is organized as, Section II give the implementation procedure
of the PPCDSS. Along with, the working and all functionality
required for use of fuzzywuzzy algorithm. Section III gives the
result analysis that performed by tacking actual symptoms for
five different patients. This validate the efficiency of the
proposed PPCDSS and gives the advantages of the proposed
and develop system. Finally Section IV, concludes the paper.

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II. IMPLEMENTATION STRATEGIES
This paper tries to improve the existing system using
Clinical Decision Support System based on Naïve bayesian
classifier. The system uses are using Data Mining classification
technique for Clinical Decision Support System (CDSS). The
system will work faster and efficient using this technique [12]
[13]. It is widely used in real-life applications because of its
simplicity and good performance both in theory and practice.
However, in large-scale problems, where huge training data are
available, such as road sign detection, the method‟s training
and test phases might be prohibitively demanding in terms of
computations. Thus, for large-scale problems the reduction of
computational complexity is essential. For the security purpose
encryption techniques with AES algorithm for preserving
privacy of patient‟s data is used [14][15]. The complete flow of
working system in step-by-step manner is as follows:
A. Stepwise Work Flow of System:
Step 1: Doctor Register with the System.
Step 2: Doctor has to login the system with his authentic
email-id and password.
Step 3: Doctor can add / edit / update /delete any number of
disease, their symptoms, and their prescription
information.
Step 4: Doctor add patient information along with the
symptoms he is suffering from to the database and
check for diagnosis.
Step 5: Using Database will provide the historical medical data
present in our database and processing with the help of
Naïve Bayesian classifier fuzzywuzzy search
algorithm.
Step 6: After calculation, the predicted result will be send to
the next level. On this level the probability of predicted
disease risk will be calculated and top three disease
having probability of more than 50% are displayed. In
this algorithm the maximum probability disease risk
will be calculated.
Step 7: Now doctor check the patient symptoms once again
and from the result generated in step 6, he suggest most
suitable prescription for patient. Finally, proper
predicted diseases will be diagnose, this will help to
give proper prescription to the patients more
effectively.
Step 8: For more proper CDSS designing, doctor review his
prescription suggested to the patient. Here he checks
that, the patient gets cure form his provided
prescription or not. If the patient gets cure then go to
step 9 or stop otherwise.
Step 9: Check for any new symptoms that the patient is
suffering from and already our CDSS data have. If any
new symptoms are identified, Retrain database by
adding symptom to that particular disease.
B. Algorithm Used
FUZZY SERACHING with NAIVE BAYES
CLASSIFIERS
Here the system uses fuzzywuzzy searching algorithm for
diagnosis of patient based on naïve bayesian classifier. The
complete description is as follows [16]:
FuzzyWuzzy Algorithm:
It is simple library and command-line general regular
expression like utility which could help you when you are in
need of approximate string matching or substring searching
with the help of primitive regular expressions.
About "approximate" or "fuzzy" string comparison and its
need:
Just imagine that you deal with information (like orders) which
is sent to you by many people. When these people mention
names of places or persons, they could bring to you problems
of two kinds:
 they make nasty typos;
 they use different variants of names;
1. For example if you are responsible for checking
incoming mail in, you may want to find letters
addressed to Indian president. You try to find all
which contains words "Narendra Modi" on envelope.
But you soon discover that sometimes people address
this person as "Priminister Narendra Modi" and
sometimes like "Mr. N. Modi" and also
"Narendrabhai Modi" (note typos).
2. In another example, if you read google and wikipedia
and found that you can compare "Barak" with
"Barack" and "Baarck" etc (may be the name of
former US president Barak Obama). With the help of
"approximate string matching algorithm", also called
"fuzzy string matching". But after you use or
implement some of algorithms you found that it is not
sufficient. You need "approximate" substring search,
and ability to specify some complex patterns (for
example country could be specified like "Russia" and
like "Russian Federation" - but it should not be mixed
with "Belarussian Republic" etc.
Naïve Bayesian classification with fuzzy matching:
Fuzzy matching is a general term for finding strings
that are almost equal, or mostly the same. Of course almost and
mostly are ambiguous terms themselves, so it is necessary have
to determine what they really mean for your specific needs?
The best way to do this is to come up with a list of steps before
starting to write any fuzzy matching code. Once you have
perform all steps, then it‟s much easier to tailor your fuzzy

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matching code to get the best results. These steps are
summarized as follows [17]:
A. Normalization
The first step before doing any string matching is
normalization. The goal with normalization is to transform the
input strings into a normal form, which in some cases may be
need to do. The most basic normalization you can do is to
lowercase and strip whitespace. And in some cases, one can
also removes all punctuation in a string.
Example:
'Happy Days' != ' happy days ', with simple normalization you
can get 'Happy Days'.lower() == ' happy days '.strip().
Output : „happy days‟
Beyond just stripping whitespace from the ends of strings, it‟s
also a good idea replace all whitespace occurrences with a
single space character. The regex function for doing this is
re.sub('s+', s, ' '). This will replace every occurrence of one or
more spaces, newlines, tabs, etc, essentially eliminating the
significance of whitespace for matching.
B. Regular Expressions
It is helpful to use regular expressions to identify significant
parts of a string, or perhaps split a string into component parts
for further matching. Create a simple regular expression to help
with fuzzy matching, because any other code if present to do
fuzzy matching will be more complicated, less straightforward,
and probably slower.
FuzzyWuzzy algorithm functions
Get match ratios with the help of following fuzzy search Ratio
expressions / functions:
1. Simple Ratio
FuzzySearch.ratio("mysmilarstring","myawfullysmilarstirng")
- 72
FuzzySearch.ratio("mysmilarstring","mysimilarstring") - 97
Along with this there are different techniques of this as Partial
Ratio, Token Sort Ratio, Token Set Ratio, Weighted Ratio etc,
Extract Result from calculations using following fuzzy extract
expression / functions are:
1. Extract One:
FuzzySearch.extractOne("cowboys", ["Atlanta Falcons", "New
York Jets", "New York Giants", "Dallas Cowboys"])
(string: Dallas Cowboys, score: 90)
Extract Top:
FuzzySearch.extractTop("goolge", ["google", "bing",
"facebook", "linkedin", "twitter", "googleplus", "bingnews",
"plexoogl"], 3)
[(string: google, score:83), (string: googleplus, score:63),
(string: plexoogl, score:43)]
In the proposed application, the above ExtractTop method is
used that gives the top three predicted diseases from which
patient may be suffering from. Result generated from it makes
easy for the doctor to calculate the actual disease from only
three most probable options.
III. RESULT AND DISCUSSION
The Step-by-step workflow of the proposed system
and the algorithm that perform its result extraction is also
explain in the above sections. Now the Experimental result
shows that Clinical Decision Support System Based on Naïve
Bayesian classifier using fuzzy string matching approach gives
better prediction result is checked experimental real time data.
We have taken actual symptoms from five patients from which
they are suffering and provide to our system. The result
generated from the system are as follows:
Patient 1: This patient is actually suffering from Thyroid
Disease
Symptoms given by patient 1 are:
Selected Symptoms: Fatigue, weight gain, Cold intolerance,
Dry or brittle hair, memory problem, irritability and
depression,
Extra added Symptoms: cholesterol level is high, slow heart
rate
Result given by the system:
Hypothyroidism
(92%)
Measles
(86%)
Rabies (also called
hydrophobia) (48%)
Graph 1: Accuracy of prediction for Patient 1
From the above graph we find out that, in terms of
diagnosis accuracy of our system with the naïve Bayes
fuzzywuzzy algorithm find out most accurate result. That is as
the patient is already suffering from thyroid and the diseases
predicted from our system is also Hypothyroidism with highest
probability of having that disease is 92%.

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Patient 2: This patient is actually suffering from Osteoporosis
Selected Symptoms: easy bone fractures, stress fractures of
feet at walking or stepping
Extra added Symptoms: lower back pain, pain in legs,
Osteoporosis (91%)
Chicken
Pox (86%)
Diabetes
Mellitus (86%)
Patient 3: This patient is actually suffering from Dengue
Selected Symptoms: easy bone fractures, stress fractures of
feet at walking or stepping
Extra added Symptoms: lower back pain, pain in legs,
Dengue (88%) Measles (86%) Chicken pox (57%)
Patient 4: This patient is actually suffering from Cardio
vascular diseases
Selected Symptoms: Persistent high blood pressure (BP), Suger
level is high,
Extra added Symptoms: damage of arteries, fatigue
Cardio vascular
diseases (89%)
Chicken pox
(86%)
Dengue (86%)
Patient 5: This patient is actually suffering from Tuberculosis
Chest pain and blood comes out with the sputum.,Cough last
from 2-3 weaks,loss of appetite,Coughing up blood,
Extra added Symptoms: general weakness,
Tuberculosis (91%) Hepatitis (49%) Chicken pox (48%)
As in case of patient 2, the probability of prediction is
not vary in most cases. That is first prediction is 91%, second
is 86% and third is 86%. This is because, the symptoms given
by the patient are generalized and that may cause all three
types of disease that obtained in the result. But instead of that
the first prediction is given 91% to Osteoporosis that is the
actual disease with which patient 2 is suffering from. Therefore
the prediction rate of our proposed system is high. The overall
analysis of five patient is given in the graph 2 below.
0
10
20
30
40
50
60
70
80
90
100
First Prediction Second Pred. Third Pred.
Graph 2: Accuracy of Prediction

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Graph 2 above shows the prediction accuracy of our
proposed system. As we have collected the actual systems from
five patient suffering from different diseases. All the symptoms
given to our developed system and the result obtained are
given in tabular format above. And in the above graph the
overall accuracy of prediction of our system is shown. For all
the time the system show the highest percentage to the disease
to the actual disease with which the patient is suffering from as
its first prediction. This gives our proposed and develop system
for CDSS using naïve bayesian classification is most accurate.
That is success rate of our proposed system is much high.
Advantages:
By designing the system like this we are able to
 Improving diagnosis accuracy for any critical diseases
 Reducing diagnosis time gives proper prescription in
much less time
 Top 3 disease prediction out of which you can choose
most probable. The success rate for the first prediction
is most of the time high
 Reducing communication overhead
 Preserving privacy of patient‟s sensitive data
IV. CONCLUSION
In this paper, the Clinical decision support system
using classification technique of data mining with naïve Bayes
algorithm is proposed. This naive Bayes with fuzzywuzzy
algorithm has excellent performance in generalization so it can
produce high accuracy in classification for diagnosis. The
patient can securely retrieve TOP three diagnosis result
according to their own preferences. With the advantage of
encryption technique, the privacy of patient sensitive data is
achieved. The processing is done on the encrypted data, so that
there is no loss in the privacy of patient‟s data while training
the CDSS. These results analysis performed evidentially
proved that proposed method shows the nice performance of
classification accuracy with the proposed algorithm.
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Implementing Clinical Decision Support System Using Naïve Bayesian Classifier

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