1.1 Introduction
Document classification is the task of grouping documents into categories based upon their content. Document classification is a significant learning problem that is at the core of many information management and retrieval tasks. Document classification performs an essential role in various applications that deals with organizing, classifying, searching and concisely representing a significant amount of information. Document classification is a longstanding problem in information retrieval which has been well studied (Russell, 2018).
As a prelude to other parts of this study, this chapter will discuss the background upon which this study was initiated, the statement of problems that led to this study, the Aim and Objectives of the study. Others are Significance of the study, Scope of work, Limitations of the Study and Definition of technical terms.
1.2 Background of Study
Contemporarily, most hospitals, medical laboratories and other health facilities make use of some kind of information system. These could be either a hospital management system or a pharmacy management system. Among other functions that these systems provide, they are mainly used in collecting patient records. These information systems stores patient records in digital format. Numerous patient data are being recorded on a daily basis which forms a large data set popularly referred to as “Big Data”.
Every day physicians and other health workers are required to work with this “Big Data” in other to provide solution. Some of the everyday tasks include information retrieval and data mining. Retrieving information from big data can be very laborious and time consuming. This has given rise to the study of text or document classification in other to aid the process of retrieving information from big data. Today, text classification is a necessity due to the very large amount of text documents that we have to deal with daily.
Document classification is the task of grouping documents into categories based upon their content. Document classification is a significant learning problem that is at the core of many information management and retrieval tasks. Document classification performs an essential role in various applications that deals with organizing, classifying, searching and concisely representing a significant amount of information. Document classification is a longstanding problem in information retrieval which has been well studied (Russell, 2018). Usually, machine learning, statistical pattern recognition, or neural network approaches are used to construct classifiers automatically. Machine learning approaches to classification suggest the automatic construction of classifiers using induction over pre-classified sample documents. In this project work we will employ machine learning in classifying health documents.
The challenges encountered that led to the execution of the research work is that, with the explosion of information fuelled by the growth of the World Wide Web it is no longer feasible for a human observer to understand all the data coming in or even classify it into categories. Also in the health sector, numerous patient records are being collected everyday and are used for analysis.
It is against the background that the developments of this software to enable health workers diagnose patients, so the software users should not give false information that is out of the software deeds. This research work is based on providing adequate information about medical records. At the end of this project, the research work, the software will be able to provide precise and concise patient records from the database.
1.3 Statement of Problem
Investigation reveals that due to the massive increase in medical documents every day (including books, journals, blogs, articles, doctors' instructions and prescriptions, emails from patients, etc.), it is becoming very challenging to handle and to categorize them manually.
One of the most challenging projects in information systems is extracting information from unstructured texts, including medical document classification. The discovery of knowledge from medical datasets is important in order to make effective medical diagnosis.
The explosion of information fuelled by the growth of the World Wide Web, it is no longer feasible for a human observer to understand all the data coming in or even classify it into categories. Also in the health sector, numerous patient records are being collected everyday and are used for analysis. How do we efficiently classify or categorize these health documents to complement easy retrieval.
1.4 Aim and Objectives of the Study
The aim of this study is to develop a system for Health Document Classification Using Machine Learning. In achieving this aim, the following specific objectives were laid out as follows to design and implement an application software that will:
- Study the various machine learning classification algorithm.
- Accurately classify the health documents either into Malaria, Diarrhea or Hypertension.
- Totally or partially eliminate the problems encountered in the existing system.
1.5 Significance of Study
The software delivered from this project work will greatly reduce the time used by doctors, physicians and other health workers in searching and retrieving documents.
Other importance of this project work includes:
- Helps students and other interested individuals that want to develop a similar application.
- It will serve as source of materials for those interested in investigating the processes involved in developing a document classification system using machine learning.
- It will serve as source of materials for students who are interested in studying machine learning.
1.6 Scope of the Study
The scope of this research focuses on the Design and Implementation of Health Document Classification Using Machine Learning. As stated earlier, statistical pattern recognition, or neural network are used in classifying documents, this project work will concentrate on using machine learning algorithm to classify document.
1.7 Limitations of the Study
During the course of this study, many things militated against its completion, some of which are:
- Time Constraint: The time frame given to accomplish this project was very short due to school academic calendar and it was carried out under pressure which made the researcher not to implement some necessary features.
- Research material: availability of research material is a major setback to the scope of the study.
- Frequent power failure: This made the researcher append more money on fuel to ensure sustainable power.
- Financial Constraint: Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet).
1.8 Definition of Terms
Document Classification: is the task of grouping documents into categories based upon their content.
Health Document: A health certificate is written by a doctor and displays the official results of a physical examination.
Machine Learning: the study and construction of algorithms that can learn from and make predictions on data.
