INTRODUCTION
1.1 BACKGROUND OF STUDY
Data mining, is the extraction of hidden predictive information from large database,
is a powerful new technology with great potential to help companies focus on the
most important information in their data warehouses. Data mining tools predict
future trends and behaviors, allowing businesses to make proactive, knowledge
driven decisions. The automated, prospective analysis offered by data mining move
beyond the analyses of past events provided by retrospective tools typical of
decision support systems. Data mining tools can answer business questions that
traditionally were too time consuming to resolve. The scour databases for hidden
patterns, finding predictive information those experts may miss because it lies
outside their expectations.
Most companies already collect and refine massive quantities of data. Data mining
techniques can be implemented rapidly on existing software and hardware
platforms to enhance the value of existing information resources, and can be
integrated with new products and system as they are brought on-line. Which
implemented on high performance client/server or parallel processing computers,
data mining tools can analyze massive database to deliver answers to questions
such as “Which client are most likely to respond to my next promotional mailing,
and Why?”
Data mining techniques are result of a long process research and product
development. This evolution began when business data was first stored on
computers, continued with improvement in data access, and more recently,
generated technologies that allow users to navigate through their data in real time.
Data mining takes this evolutionary process beyond retrospective data access and
navigation to prospective and proactive information delivery.
Data mining is ready for application in the business community because it is
supported by three technologies that are sufficiently mature: massive data
collection, powerful multiprocessor computers and data mining algorithms. In this
evolution from business data to business information, each new step has built upon
the previous one. For example, dynamic data access is critical for drill-through in
data navigation applications, and the ability to store large database is critical to
data mining.
The file management is obsolete in developed countries like the United States
where and in developing countries like Nigeria the file system is still processed
manually in most medical centers; this is as a result of low standard of technology.
It was clear that computer is everywhere in Nigeria. These computers are for
money making and as a result of this, our hospitals lack computerized services, but
with the help of data mining we can also computerize our hospitals.

1.2 STATEMENT OF THE PROBLEM
The problem of data mining has become very crucial in areas of privacy of data.
Specifically regarding the source of the data analyzed for certain purpose. My
research in Owerri General Hospital here reveals that patients visit the hospital and
they waste a lot of time. The patients waits for the nurses or the attendants to get
their data and there are volumes of files to search through before the patients files
is finally retrieved or the patients might forget his/her card when visit the hospital.
It there means that the patient‟s data cannot be found due to the fact that the Nurse
does not know the patients number. In this case it is either that the patients is
denied treatment by the Doctor or the Nurses will check through the volumes of
files in order to retrieved his/her data, this is time consuming problem. There is a
problem of misplacing of patients data, the non-availability of relevant forms like
x-ray/laboratory forms and chats (pressure chats temperature) and requirement of
more workers to carry the folders into the consulting rooms as an evident.
Moreover, there are mistakes in entering patient‟s records. Two patients might be
given the same number and there could be wrong spelling and loss of important
information. There is also lack of space for storing all the files and also due to
carelessness on the part of staff.
Furthermore, volume of work for the hospital staff is much; this is because the
ratio of patients to staff of Owerri General Hospital is so much. So staffs are over
worked and they hurry through their duty, hence they carry out such duties lousily
which makes the Doctors unfriendly to their patients.

1.3 OBJECTIVE OF STUDY
Objectives of this research work are to:
Create a data warehouse for storage of patient‟s information thereby
eliminating manual file storage of patient‟s records.
Design a good data mining tool that will help in easy retrieval of patient
information thereby reducing time wastage and improve service delivery.
The data mining tool will be able to discover hidden pattern in large volume
of data which will help in good decision making.

1.4 SIGNIFICANCE OF STUDY
This research will cover the creation of good database system for the management
of patient‟s records in Owerri general hospital and also provide efficient data
mining tool for easy retrieval of data and discovery of hidden patterns in large
volume of data.

1.5 SCOPE OF STUDY
This research work will be carried out on data mining for medical record system of
Owerri General Hospital. The work reported in this research could be viewed as a
step towards enhancing databases with functionalities.

1.6 LIMITATION OF STUDY
Data mining record is limited in that; some manual operation will still be needed to
carry out the operation effectively. There was some constraints encountered during
collection of data, poor data collection becomes apparent due to interviewing of
hospital representatives like the consultants. Nurses, Doctors, Hospital attendants
who were reluctant to disclose important information and statistical data which
otherwise would have been relevant to this research, due to hospital secret which
breeds some indifferent attitudes towards that.
It takes a long time and large commitment of resources to get a good result,
unavailability of text and materials on this topic, made gathering of facts very
difficult, some of the facts were gathered from the internet, which is quite
expensive.

1.7 DEFINITION OF TERMS
Data Mining: Can be defined as “The nontrivial extraction of implicit, previously
unknown, and potentially useful information from data, and “The science of
extracting useful information from large data set or databases”.
It involves sorting through large amounts of data and picking out relevant
information.
Information Retrieval: The act of locating quantities of data stored in a Database
and producing useful information from the data.
Information Processing: A method of organizing, processing and extracting
information to be easily stored, retrieved, searched and updated.
Record: It is a unit of data representing a particular transaction or a basic element
of a file consisting in turn a number of interrelated data elements.
Hospital: A hospital is an institution of medical treatment of the sick and injured
people.
Model: Is a pattern or mathematical/symbolic representation of real life
System and or abstract system behaviors.
Artificial Intelligence: It is a branch of computer science that is dedicated to the
study of the ways in which computers can be used to emulate or duplicate most
human function.
Knowledge Base: Is an organized collection of declarative and procedural
relationships that represents expertise in a focused area