IRJET - Chronic or Acute Disease with Doctor Specialist using Data Mining

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 02 | Feb 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 3125
Chronic or Acute Disease with Doctor Specialist Using Data Mining
Suvathi A1, Thiyaneswari R2, Jagan S3
1,2Final year students, Agni college of Technology
3Dr S jagan,Dept. of Computer Science Engineering, Agni college of Technology , TamilNadu, India
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Abstract - For ceaseless sickness, Medical history
reproduction is fundamental for review database
investigations. It significantly affects the measurement of
further analysis result. It shows the prescription development
structure for the medicinal history of patients with chronic, or
acute diseases. The main goal is to identify the disease name
and predicting the solutions. Suggesting the medicine to the
patients for using data mining (prediction) user find the
location of doctor specialist from the analyzed datasets.
Key Words: Data mining Techniques, Medications,
chronic or acute diseases.
1. INTRODUCTION
Nowadays, people are keen interest in taking care about
their health. So people are running behind the medicines
such as ayurveda, siddha etc. Medication is one of the
important roles in our day-to-day life. In this most of the
diseases are occurred by some deficiency and some health
disorders even from small kids to age peoples. There are
some symptoms knowing and some are unknowing to the
people. With the symptoms it can be judged by two diseases.
One of them is the chronic and other is theacutedisease.The
chronic disease is said to be a long-lastingdiseasethatissaid
to be of cancer, diabetes, etc. Acute disease is said to be a
short term disease such as fever. By both diseases a proper
medication is a given to the patients by a specific doctor
specialist in our locality. There can also be somediseasesaid
as a communicable and non-communicable disease.
Communicable diseases are disease which can be easily
transmitted from the affected person to the unaffected
person. For example if one person has cold, cough it get
easily transmitted to the person who is communicating with
them or using their personal things. Non-communicable
diseases are the disease which is an internal communicated
disease. For example, AIDS is one of the best examples
transmitted internally through blood. We only created the
incurable disease by some irregularactivities.Diseasecanbe
any form like bacteria, fungi or virus in our natural world. It
has much prevention and curing through medication in
proper. One word we are saying that doctors are equal to
god because they give life to us by providing some
medication. In this we have to specify the doctor specialist
with a specific department (i.e. entomology,cardiologist etc)
In this paper chronic or acute can be finding out, and
identifying by the symptoms with the duration. So, we are
collecting the datasets and giving a predicteddata usingdata
mining. Data mining is one of the methods to predict the
future basis.
1.1 RELATED WORK
In this session discussed about some papers
A. Dhara B.Mehta, Nirali C.Varnagar proposed
Newfangled Approach for Early Detection and
Prevention of Ischemic Heart Disease using Data
Mining. The main aim of this paper is to detect the
risk of Ischemic stroke at early stage by using Naïve
Bayes, Support Vector Machine (SVM).
B. R.Rosso, G.Munaro, O.Salvetti, S.Colantonio,
F.ciancitto aims to Chronious: An open, Ubiquitous
and Adaptive Chronic Disease Management
Platforms for Chronic Obstructive Pulmonary
Disease (COPD), Chronic Kidney Disease (CKD) and
Renal Insufficiency. They are using smart patient
monitor unit (PMU) device provided using the
Bluetooth. By using the algorithm of decision
support system.
C. Masha Soudi Alambari, Mehdi Teimouri, Farshad
FarzadFar, Amir Hashemi-Meshkini have aimed to
Disease Detection in Medical Prescriptions using
Data Mining Tools. In this paper a set of data
collected from 1412 prescriptions is used with 414
kinds of drugs. They have used Naïve Bayes
algorithm.
D. M.Ilayaraja and T.Meyyappan aimed to Mining
Medical Data to Identify Frequent Diseases using
Apriori algorithm. This study useful to identify
frequent diseases in a large medical dataset.
E. Purnomo Husnul Khotimah, Yuichi Sugiyama,
Masatoshi Yoshikawa, Akihiro Hamasaki, Osamu
Sugiyama, Kazuya Okamoto, and Tomohiro Kuroda
have aimed to develop a Medication Episode
Construction Framework for Retrospective
Database Analyses of patients with Chronic
Diseases. This system presents a medication
episode construction for the medical history of
patients with chronic diseases. In this they used the
multitherapy datasets by Allen’s method.
1.2 METHODOLOGY
A. DATA
The data collected is to predict diseases, chronic, or acute
diseases, symptoms, medicines, location, and doctor

specialist name. The dataset which we collected is
comfortable for the users and it is text format.
B. SYMPTOMS
User can enter their symptoms in a text format. C.
DURATION It includes the patient’s symptoms duration.(i.e.
days, month)
D. DISEASES
By analyzing the symptoms and duration, we can specialist
predict the disease using data mining.E.CHRONIC,OrACUTE
DISEASES From the symptoms, duration, diseases we are
going to predict whether it is chronic or acute disease.
F.LOCATION
By knowing the disease the concern specialist is
suggested with the location. G.DOCTOR SPECIALIST In this,
we are checking with high percentage in which doctors are
famous in their specific department anddisplayedwiththeir
name.
2. NAÏVEBAYES
NAÏVE BAYES is a classification of objects. It is strong and an
attribute of data points with independently. It also includes
spam filters (unrelated information), text analysis, and
medical diagnosis. This is also called as simple bayes or
independence bayes. It wassupportedbyoracledata mining.
It is easy to build and in a particular use of large datasets.
The formula is as follows P(c/x) = p(x/c) p(c)/p(x) Where
P(c/x) is the posterior probability of class P(c) is the prior
probability of class. P(x/c) is the likelihood which is the
probability of predictor given in a class. P(x) is the prior
probability of predictor.
3. RESULT
The dataset includes chronic, or acute diseases, locations,
doctor specialist. This method implemented using Naïve
Bayes by collecting the datasets and giving percentage to
each doctor for their better treatment with the specified
location.
4. CONCLUSION
This research work proposes a Naïve Bayes data mining
techniques. It is clear that enter the symptoms we can
predict the diseases and doctor specialist. Further it can
improve the specialist number and notification sends to the
person it can be easier
REFERENCES
[1] F.Sjoqvist and D.Birkett, “Drug utilization”, Introduction
to Drug Utilization Research. (WHO booklet) New York:
WHO office of publications, pp. 76-84, 2003.
[2] H.Gardarsdottir, P.C. Souverein, T.C. Egberts, and E. R
Heerdink, “Construction of drug treatment episodes from
drug-dispensing histories is influenced by the gap length”,
Journal of clinical epidemiology, Vol. 63, no. 4, pp. 422-427,
2010.
[3] A.Pottegard and J.Hallas, “Assigningexposuredurationto
single prescriptions by use of the waiting time distribution,
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2013.
[4] Y.Wang, p.Li, Y.Tan, J.-j.Ren, and J.-s.Li,“a shareddecision
making system for diabetics medication choice utilizing
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vol. 21,no. 5,pp.1280-1287, Sep. 2016
[5] Japan Diabetics society, “treatment guide for diabetics
2012-2013, -“Jpn. Diabetics soc., Tokyo, Japan, 2012.
[6] M.Pawaskar, M.Bonafede, B.Johnson, R.Fowler,
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[7] P.H. Khotimah, Y.Sugiyama, M.Yoshikawa, A.Hamasaki,
K.Okamoto, and T.Kuroda, “Revealing oral medication
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BIOGRAPHIES
SUVATHI A B.E.,
Final year CSE
Agni College of Technology, Chennai
THIYANESWARI B.E.,
Final year CSE
Agni College of Technology, Chennai

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