Aveneu Park, Starling, Australia

Pragati techniques are applied to predict the

Pragati Shukla
[email protected]
AISSMS College of Engineering

Simran lal
[email protected]
AISSMS College of Engineering

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 Gauri Kumbhar
[email protected]
AISSMS College of Engineering

Susmita Kulkarni                                                         Mrs.
V. V. Waykule

[email protected]                                        [email protected]

AISSMS College of Engineering                                  AISSMS College of Engineering



Electronic Health
Records (EHRs) are the main source of information for assessment, diagnosis,
and treatment of disease in clinical care. An EHR typically contains a patient’s
historical health data, collected over several years of patient care. This data
includes both physician’s clinical notes written in
unstructured text recording their observations, assessments, and plans, as well
as structured data such as ordered medications, vital signs measurements,
laboratory test results, and procedures conducted. The system takes input and
helps the user to predict the disease. The result for the same is provided. The
proposed system assists doctor to predict disease correctly and the prediction
makes patients and medical insurance providers benefited. The use of EHRs are
very limited when the scenario in our country is taken into account. This can
also benefit the physician since the patient history will be readily available
and in a structured format. Through the visits the results will be stored and a
record will be maintained. Thus, our system will enhance the usage of EHR to
store data as well as to predict the disease accurately and efficiently.


Keywords:- data mining; clinical decision support
system; expert application; disease prediction; C4.5.


Computational health
informatics is an emerging research topic which involves various sciences such
as biomedical, medical, nursing, information technology, computer science and
statistics . Data mining techniques are applied to predict the effectiveness of
surgical procedures, medical tests, medication, and the discovery of relationships
among huge clinical and diagnosis data. In medical science, doctor’s facilities
introduced different data frameworks with a lot of information to manage
medical insurance and patient information but unfortunately, data are not mined
to discover hidden information for effective decision. Clinical test outcomes
are regularly made on the basis of doctor’s perception and experience rather
than on the knowledge enrich data masked in the database and sometimes this
procedure prompts inadvertent predispositions, doctors expertise may not be
capable to diagnose it accurately which affects the disease diagnosis system .
In health care sector, the term information mining can mean to analyze the
clinical information to predict patient’s health status. So discovering
interesting pattern from health care data, different data mining techniques are
applied with statistical analysis, machine learning and database technology.



The current status of
the healthcare sector in India is associated with low public spending (1% of
GDP), high out-of pocket payments (71%), a high level of anemia among young
women (56%), high infant mortality (47/1,000 live births), and high maternal
mortality (212/100,000 live births), etc. India has a mixed system of
healthcare consisting of a large number of hospitals run by the Central
Government and State Government as well as the private sector. In general, the
level of use of ICT(Information and Communication Technology) in the healthcare
sector in the country has been lower in comparison to other countries. At the
same time, both union and State Governments are working on several fronts to
make use of the opportunities covered by ICT. Private sector hospitals are also
in the process of implementing ICT projects, including electronic patient

Some of the corporate
hospitals in India, such as Max Health, Apollo, Sankara Nethralaya, Fortis,
etc., have implemented integrated ICT systems in place, covering all aspects,
i.e., registration and billing as well as laboratory and clinical data. Max
Healthcare hospitals started implantation of EHR in its hospitals in 2009 and
achieved Stage 6 level of the EMR Adoption Model, which is used by the HIMSS
for assessment of the level of adoption of EMR systems in any hospital. Max
Healthcare Group received the recognition for two of its hospitals East Wing,
Saket and West Wing, Saket, New Delhi in 2012.

However, even in
private hospitals, EMRs are rarely exchanged between hospitals. These remain in
the same hospital and are referenced when the patient visits again. There is no
authentic report on the number of patients whose EMRs/EHRs have been stored so


With the increase in
health care facilities, it is also necessary to store patient information for
the ease of the physician and the government, so that better measures can be
taken in future. Since EHRs are used in many private organizations for storing
patient information, the same can be used to mine data and trace out patterns for
better understanding. This can be done using various data mining techniques and
the discovered patterns may help doctors for better decision making in the


The proposed system
assists doctor to predict disease correctly and the prediction makes patients
and medical insurance providers benefited. The system focuses on diagnosis of
diabetes as it is a great threat to human life worldwide. The system uses the
Decision Tree, C4.5 Algorithm as supervised classification model. Finally, the
proposed system calculates the accuracy of C4.5 and the experimental result
demonstrates that the C4.5 provides better accuracy for diagnosis of diabetes.
The proposed system will help in quick clinical decision making.



Supervised machine
learning is a machine learning algorithm that uses a labeled dataset for
prediction. Labeled Data means an output is associated with every input. It means
you have input variables (x) and an output variable (Y) and you use an
algorithm to learn the mapping function from the input to the output.

Y = f(X)

The goal is to
approximate the mapping function so well that when you have new input data (x)
you can predict the output variables (Y) for that data. In other words,
supervised learning builds a model using this dataset that can make a
prediction of the new or unseen dataset. This unseen or new dataset is called
training dataset and helps in validating the model. Supervised algorithms can
be used for classification and regression. Classification algorithm(s) like
Support Vector Machines, Naive Bayes, Decision trees can be used to build the
classifier for a system.


Decision tree builds classification or regression models in
the form of a tree structure. It breaks down a dataset into smaller subsets
while at the same time an associated decision tree is incrementally developed.
The final result is a tree with decision nodes (has two or more
branches) and leaf nodes (represents classification/decision). The topmost
decision node corresponds to the best predictor called root node. Decision
trees can handle both categorical and numerical data. 


Decision trees generated
by C4.5 are used for classification. C4.5 is often referred to as
a statistical classifier. C4.5 algorithm is described as “a landmark
decision tree program that is probably the machine learning workhorse most
widely used in practice to date”. Hence,also being used in the proposed
system. C4.5 builds decision trees from a set of training data using the
concept of information entropy. C4.5 chooses the attribute of the data
that most effectively splits its set of samples into subsets enriched in one
class or the other. The splitting criterion is the normalized information
gain (difference in entropy). The attribute with the highest
normalized information gain is chosen to make the decision. The C4.5 algorithm
then recurs on the smaller sublists.




The overall system
design consists of following modules: (a) Data Collection. (b) Preprocessing
(c) Data classification (d) Prediction of Output. Through the proposed
application user (doctor, patient, physician etc.) can input the attribute
values of disease and send it to the server with the help of internet. After
applying the data mining approach the predicted result can be viewed on the
user GUI. On the server, admin can load dataset of different diseases and apply
different data mining algorithms to train dataset. Requested user inputs are
collected and processed on server to predict the diagnosis result. For
analyzing healthcare data, major steps of data mining approaches like
preprocess data, replace missing values, feature selection, machine learning
and make decision are applied on train dataset and ready to classify the test
dataset. The system architecture is shown in Figure 1.

Figure 1: System Architecture


 An expert system is proposed for predicting
the diseases like diabetes using data mining classification technique. The
system gives benefit to the doctors, physicians, medical students and patients
to make decision regarding the diagnosis of the diseases. The system uses C4.5
algorithm and uses the input features to give a decision as a result. Given the
use of EHRs to form a decision tree the system thus encourages the uses of
EHRs, so that the physicians have a structured representation of patient information,
that can be easily available for them to use. Through the visits the results
will be stored and a record will be maintained. Thus, our system will enhance
the usage of EHR to store data as well as to predict the disease accurately and


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