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International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 3 Issue 5, August 2019 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD26774 | Volume – 3 | Issue – 5 | July - August 2019 Page 1607
Hospital Medicine Classification using Data Mining Techniques
Yin Yin Cho1, Khin Htay2, Win Myat Thuzar1
1Information Technology Supporting and Maintenance Department,
2Faculty of Information Science,
1,2University of Computer Studies, Meiktila, Myanmar
How to cite this paper: Yin Yin Cho | Khin
Htay | Win Myat Thuzar "Hospital
Medicine Classification using Data Mining
Techniques"
Published in
International
Journal of Trend in
Scientific Research
and Development
(ijtsrd), ISSN: 2456-
6470, Volume-3 |
Issue-5, August 2019, pp.1607-1609,
https://doi.org/10.31142/ijtsrd26774
Copyright © 2019 by author(s) and
International Journalof Trendin Scientific
Research and Development Journal. This
is an Open Access article distributed
under the terms of
the Creative
CommonsAttribution
License (CC BY 4.0)
(http://creativecommons.org/licenses/by
/4.0)
ABSTRACT
Data mining is a process which finds useful patterns from large amount of
data. This paper is analyzing decision trees technique using the medicinedata
from central medical store, department of health. The results are support to
best solution to share many type of medicine for manager at Central Medical
store. This Central Medical store is supporting medicine to hospitals from
upper Myanmar.
KEYWORDS: Data mining, Decision Trees
1. INTRODUCTION
Data mining techniques and algorithms such as classification, clustering, etc,
helps in finding the patterns to decide upon the future trends in businesses to
grow. Data mining has wide application domain almostineveryindustrywhere
the data is generated. That’s why data mining is considered one of the most
important frontiers in database and information systems and one of the most
promising interdisciplinary developments in Information Technology. Data
mining is a business process for exploring large amounts data to discover
meaningful patterns and rules large amount of data in a computer system are
often required to receive that data rapidly in a variety of sequences and
combinations. Problems often result with this system whether the system uses
a database of individual files, because the ways in which individual data items
are stored within the computer system do not reflect the underlying structure
of data that exit in the business world.
This system is developed to provide the procedure for the
Central Medical Stored sub-deport from Department of
Health. Central Medical Stored sub-deport is deliver
medicines to hospital from upper Myanmar. It has many
kinds of medicines for many types of patients at hospital.
Medicine delivery has two types. These are ordinary and
special case of deliver. Ordinary deliver has one or twice per
month for consider medicines. Special case depends on
saturation of case i.e special disaster, epidemic disease, and
special disease case.
2. Background of the system
2.1. History of C.M.S.D [Mandalay]
History of C.M.S.D [Mandalay] is the following:
Old C.M.S.D [Mandalay] location on 73rd street, between
27th and 28th street, Mandalay
Date of opening and duration :-1.10.1968to30.10.1993,
{25years}
Ownership:- Ministry of Co-operative
Present C.M.S.D [Mandalay] 1993- Granted from
government and construction began
Location:- On 62nd street, between 38th and 39th street,
Maha Aung Myae township, Mandalay
Beginning of service: - 6.12.1993
Area of new location: - 8.7742 Acre.
2.2. Function of C.M.S.D [Mandalay]
1. Indent Medical Supply from Yangon C.M.S.D.
2. Storage of Medicine and Medical requirements for
distribution.
3. Storage for transit.
4. Distribution of Medicine and Medical requirement
[Indenting System].
5. Notice by mail or telephone or telegram to respective
township or Hospitals to get medicineortransitmedical
supply]
3. Related Work
In [2], author has used the database of an Electronic store to
see whether a person buys a laptop or not with decision tree
algorithm. There are class-labeled training tuples from the
electronic store. Each attribute taken is of a discrete value.
The class-labeled attribute buys _laptop, has two distinct
values (yes, no). The database of Electronicsstoreshowsthat
the attribute age has the maximum or highest Information
Gain and that the age attribute also has the minimum Gini
index, therefore, resulting in a maximum reduction in
impurity of the tuple in this. By taking age as the splitting
attribute. In [4], Author use Weka tool to demonstrate the
experimental results of the dataset with 10- fold cross
validation .The datasets are collected from UCI / KEEL
repositories to test the accuracy of ID3 and C4.5 classifiers.
WEKA is a machine learning that supports many algorithms
of data mining. The tool is programmed in Javalanguagewith
GUI for interacting with data files in additional to produce
visual results.Thefollowingtableshowsthecharacteristicsof
selected datasets with nominal attributes and no missing
values. We can handle other features of datasets that contain
IJTSRD26774
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD26774 | Volume – 3 | Issue – 5 | July - August 2019 Page 1608
both continuous and discrete attributes with missing values.
Whereas the ID3 algorithm cannot handle these features by
using unsupervised discretization to convert continuous
attributes into categorical attributes and pre-processing to
replace the empty cells.
4. Use of Methods
Most data mining techniques have existed, at least as
academic algorithms, for decades the oldest, survival
analysis, actually date’s back centuries. Data mining has
caught on in a bit way, increasing dramatically since the
1990s.
Decision trees are one of the most powerful directed data
mining techniques, because we can use them awiderangeof
problems and they produce models that explain how they
work. Decision trees are related to table look up models.
Decision trees extend idea in two ways. First, decision trees
recursively split data into smaller and smaller cells which
are increasingly ‘pure’ in the sense of having similar values
of the target. The decision trees algorithm treats each cell
independently. Second, the decision trees use the target
variable to determine how each input should be partitioned.
In the end, the decision trees break the data into segments,
defined by the splitting rules at each step.
A model that can be expressed as a collection of rules is very
attractive. Rules are readily expressed. A decision tree is a
hierarchical collection of rules that describe how to divide a
large collection of record into successively smallergroupsof
record.
Decision trees may be n-ary, n>=2. There is a special node
called root node. All nodes drawn with circle are called
internal nodes. All nodes drawn with rectangle boxes are
called terminal nodes or leaf nodes. Edges of a node
represent the outcome for a value of the node. In a path, a
node with same label is never repeated. Decision tree is not
unique, as different ordering of internal nodes can give
different decision tree.
5. Function of Decision Trees for C.M.S.D [Mandalay]
.Matrix of
Supplementary
Division State Apr May June July Aug Sept Oct Nov Dec Jan Feb Mar
Mandalay * A A * S S
Sagaing * A A * S S
Magwe * S S * A A
Kachin * S S * A A
Chin * S S * A A
Shan[North] * A A * S S
*Indent, A Annual, S Supply
Starting the system, entry item can be used to input the Hospital name, Division, City, Township, Village and level of Hospital.
The user selects item sub-menu, then input form will be appeared. If new records append the table, choose to edit or update
require hospital information choice. The user received the medicines from central medical stored to warehouse by entire his
drug type. User must choose Division or State during the period by Annual or Supply, system accept the request form. But the
request period is not arrived, system refuse the request form by the message.
If the case of Emergency are [floating, fire, diseases, etc.] user can request from Emergency Medicine. If Annual or Supply are
issued by drug type, if duration time, accept the request form. If not the duration time, refuse the requestform.Ifuserrequests
the Emergency Medicine, issue form will appear, and then user can choose the entire drug type. Issue form item consists of
[Injection, Liquid, Tablet] and others.
Issue form
International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD26774 | Volume – 3 | Issue – 5 | July - August 2019 Page 1609
Request form of medicine
Issue of Emergency Medicine
6. Conclusion
Data mining has importance regarding finding the patterns,
forecasting, discovery of knowledgeetc,in differentbusiness
domains. Medical research and practice are the important
areas of application for decision tree techniques. Decision
tree is most useful in diagnostics of variousdiseasesand also
use for Heart sound diagnosis.
The efficiency of various decision trees can be analyzed
based on their accuracy and time taken to derive the tree.
This paper provides students and researcher some basic
fundamental information about decision tree, tools and
applications. This paper is analyzing the classification of
medicine record used decision methods. These are support
to valuable information for manager and staff of that
department.
7. References
[1] AR’s. Bharati M. Ramageri, “Data Mining Techniques
and Applications”, India
[2] Bhumika Gupta, "Analysis of Various Decision Tree
Algorithms for Classification in Data Mining",
International Journal of Computer Applications (0975 –
8887) Volume 163 – No 8, April 2017.
[3] Gordon S. Linoff, Michael J. A. Berry, “Data Mining
Techniques”, 3rd edition
[4] Himani Sharma, "ASurveyon DecisionTreeAlgorithms
of Classification in Data Mining", SRM University,
Chennai, India
[5] Radhwan H. A. Alsageer,"Popular Decision Tree
Algorithms of Data Mining Techniques: A Review",
IJCSMC, Vol. 6, Issue. 6, June 2017, pg.133 – 142
[6] Addison Wesley. Database Management System. Date
C. J. 7th edition
[7] Dr. Debasis Samantha, Classification: Decision Tree
Induction, Associate Professor. Department of
Computer Science and Engineering.

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Hospital Medicine Classification using Data Mining Techniques

  • 1. International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 3 Issue 5, August 2019 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470 @ IJTSRD | Unique Paper ID – IJTSRD26774 | Volume – 3 | Issue – 5 | July - August 2019 Page 1607 Hospital Medicine Classification using Data Mining Techniques Yin Yin Cho1, Khin Htay2, Win Myat Thuzar1 1Information Technology Supporting and Maintenance Department, 2Faculty of Information Science, 1,2University of Computer Studies, Meiktila, Myanmar How to cite this paper: Yin Yin Cho | Khin Htay | Win Myat Thuzar "Hospital Medicine Classification using Data Mining Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456- 6470, Volume-3 | Issue-5, August 2019, pp.1607-1609, https://doi.org/10.31142/ijtsrd26774 Copyright © 2019 by author(s) and International Journalof Trendin Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (CC BY 4.0) (http://creativecommons.org/licenses/by /4.0) ABSTRACT Data mining is a process which finds useful patterns from large amount of data. This paper is analyzing decision trees technique using the medicinedata from central medical store, department of health. The results are support to best solution to share many type of medicine for manager at Central Medical store. This Central Medical store is supporting medicine to hospitals from upper Myanmar. KEYWORDS: Data mining, Decision Trees 1. INTRODUCTION Data mining techniques and algorithms such as classification, clustering, etc, helps in finding the patterns to decide upon the future trends in businesses to grow. Data mining has wide application domain almostineveryindustrywhere the data is generated. That’s why data mining is considered one of the most important frontiers in database and information systems and one of the most promising interdisciplinary developments in Information Technology. Data mining is a business process for exploring large amounts data to discover meaningful patterns and rules large amount of data in a computer system are often required to receive that data rapidly in a variety of sequences and combinations. Problems often result with this system whether the system uses a database of individual files, because the ways in which individual data items are stored within the computer system do not reflect the underlying structure of data that exit in the business world. This system is developed to provide the procedure for the Central Medical Stored sub-deport from Department of Health. Central Medical Stored sub-deport is deliver medicines to hospital from upper Myanmar. It has many kinds of medicines for many types of patients at hospital. Medicine delivery has two types. These are ordinary and special case of deliver. Ordinary deliver has one or twice per month for consider medicines. Special case depends on saturation of case i.e special disaster, epidemic disease, and special disease case. 2. Background of the system 2.1. History of C.M.S.D [Mandalay] History of C.M.S.D [Mandalay] is the following: Old C.M.S.D [Mandalay] location on 73rd street, between 27th and 28th street, Mandalay Date of opening and duration :-1.10.1968to30.10.1993, {25years} Ownership:- Ministry of Co-operative Present C.M.S.D [Mandalay] 1993- Granted from government and construction began Location:- On 62nd street, between 38th and 39th street, Maha Aung Myae township, Mandalay Beginning of service: - 6.12.1993 Area of new location: - 8.7742 Acre. 2.2. Function of C.M.S.D [Mandalay] 1. Indent Medical Supply from Yangon C.M.S.D. 2. Storage of Medicine and Medical requirements for distribution. 3. Storage for transit. 4. Distribution of Medicine and Medical requirement [Indenting System]. 5. Notice by mail or telephone or telegram to respective township or Hospitals to get medicineortransitmedical supply] 3. Related Work In [2], author has used the database of an Electronic store to see whether a person buys a laptop or not with decision tree algorithm. There are class-labeled training tuples from the electronic store. Each attribute taken is of a discrete value. The class-labeled attribute buys _laptop, has two distinct values (yes, no). The database of Electronicsstoreshowsthat the attribute age has the maximum or highest Information Gain and that the age attribute also has the minimum Gini index, therefore, resulting in a maximum reduction in impurity of the tuple in this. By taking age as the splitting attribute. In [4], Author use Weka tool to demonstrate the experimental results of the dataset with 10- fold cross validation .The datasets are collected from UCI / KEEL repositories to test the accuracy of ID3 and C4.5 classifiers. WEKA is a machine learning that supports many algorithms of data mining. The tool is programmed in Javalanguagewith GUI for interacting with data files in additional to produce visual results.Thefollowingtableshowsthecharacteristicsof selected datasets with nominal attributes and no missing values. We can handle other features of datasets that contain IJTSRD26774
  • 2. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD26774 | Volume – 3 | Issue – 5 | July - August 2019 Page 1608 both continuous and discrete attributes with missing values. Whereas the ID3 algorithm cannot handle these features by using unsupervised discretization to convert continuous attributes into categorical attributes and pre-processing to replace the empty cells. 4. Use of Methods Most data mining techniques have existed, at least as academic algorithms, for decades the oldest, survival analysis, actually date’s back centuries. Data mining has caught on in a bit way, increasing dramatically since the 1990s. Decision trees are one of the most powerful directed data mining techniques, because we can use them awiderangeof problems and they produce models that explain how they work. Decision trees are related to table look up models. Decision trees extend idea in two ways. First, decision trees recursively split data into smaller and smaller cells which are increasingly ‘pure’ in the sense of having similar values of the target. The decision trees algorithm treats each cell independently. Second, the decision trees use the target variable to determine how each input should be partitioned. In the end, the decision trees break the data into segments, defined by the splitting rules at each step. A model that can be expressed as a collection of rules is very attractive. Rules are readily expressed. A decision tree is a hierarchical collection of rules that describe how to divide a large collection of record into successively smallergroupsof record. Decision trees may be n-ary, n>=2. There is a special node called root node. All nodes drawn with circle are called internal nodes. All nodes drawn with rectangle boxes are called terminal nodes or leaf nodes. Edges of a node represent the outcome for a value of the node. In a path, a node with same label is never repeated. Decision tree is not unique, as different ordering of internal nodes can give different decision tree. 5. Function of Decision Trees for C.M.S.D [Mandalay] .Matrix of Supplementary Division State Apr May June July Aug Sept Oct Nov Dec Jan Feb Mar Mandalay * A A * S S Sagaing * A A * S S Magwe * S S * A A Kachin * S S * A A Chin * S S * A A Shan[North] * A A * S S *Indent, A Annual, S Supply Starting the system, entry item can be used to input the Hospital name, Division, City, Township, Village and level of Hospital. The user selects item sub-menu, then input form will be appeared. If new records append the table, choose to edit or update require hospital information choice. The user received the medicines from central medical stored to warehouse by entire his drug type. User must choose Division or State during the period by Annual or Supply, system accept the request form. But the request period is not arrived, system refuse the request form by the message. If the case of Emergency are [floating, fire, diseases, etc.] user can request from Emergency Medicine. If Annual or Supply are issued by drug type, if duration time, accept the request form. If not the duration time, refuse the requestform.Ifuserrequests the Emergency Medicine, issue form will appear, and then user can choose the entire drug type. Issue form item consists of [Injection, Liquid, Tablet] and others. Issue form
  • 3. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD26774 | Volume – 3 | Issue – 5 | July - August 2019 Page 1609 Request form of medicine Issue of Emergency Medicine 6. Conclusion Data mining has importance regarding finding the patterns, forecasting, discovery of knowledgeetc,in differentbusiness domains. Medical research and practice are the important areas of application for decision tree techniques. Decision tree is most useful in diagnostics of variousdiseasesand also use for Heart sound diagnosis. The efficiency of various decision trees can be analyzed based on their accuracy and time taken to derive the tree. This paper provides students and researcher some basic fundamental information about decision tree, tools and applications. This paper is analyzing the classification of medicine record used decision methods. These are support to valuable information for manager and staff of that department. 7. References [1] AR’s. Bharati M. Ramageri, “Data Mining Techniques and Applications”, India [2] Bhumika Gupta, "Analysis of Various Decision Tree Algorithms for Classification in Data Mining", International Journal of Computer Applications (0975 – 8887) Volume 163 – No 8, April 2017. [3] Gordon S. Linoff, Michael J. A. Berry, “Data Mining Techniques”, 3rd edition [4] Himani Sharma, "ASurveyon DecisionTreeAlgorithms of Classification in Data Mining", SRM University, Chennai, India [5] Radhwan H. A. Alsageer,"Popular Decision Tree Algorithms of Data Mining Techniques: A Review", IJCSMC, Vol. 6, Issue. 6, June 2017, pg.133 – 142 [6] Addison Wesley. Database Management System. Date C. J. 7th edition [7] Dr. Debasis Samantha, Classification: Decision Tree Induction, Associate Professor. Department of Computer Science and Engineering.