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A System For Health Document Classification Using Machine Learning
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CHAPTER FIVE SUMMARY AND CONCLUSION
5.0 INTRODUCTION
This chapter summarizes and concludes the project work; it also gives recommendations and insight to future work.
5.1 SUMMARY
In this project work we were able to succeed in applying Natural Language Processing which is a branch of Machine Learning to Classifying Health related documents. We made use of the OpenNLP Application Programming Interface which is a Java API for training a model and classifying the documents. We make use of Materialize which is a HTML5, CSS and JavaScript framework for building the user interface. The software is also built using the Model-View-Controller (MVC) architecture.
5.2 RECOMMENDATION
To properly use the system we recommend the following:
1. The system can be hosted online on a Tomcat server, so that all users can access it from their respective locations (details of this can be found in chapter four).
2. Medical Personnel should be trained on how to use the system.
3. The model should be properly trained to ensure accurate classification by the system. a poorly trained model will lead to erroneous classification.
5.3 FUTURE WORK
Due to the limited time involved in developing this project work, some key features could not be integrated, it is my recommendation that in future work, the following features be added.
1. A crawler should be implemented such that the model is constantly being updated from the internet.
2. When there is new data added to the model from the internet, a listener (should be implemented) that triggers the retraining of the algorithm should be notified.
5.4 CONCLUSION
In conclusion we can see that applying Natural Language Processing to classification of text and text based documents is the most effective instead of using other machine learning techniques such as clustering which can be regarded as over kill. Natural language processing has a lot of potential outside document classification; its relevance has been seen in the area of sentiment analysis. It is my recommendation that further research be carried out in the field of Natural Language processing.
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ABSRACT - [ Total Page(s): 1 ]ABSTRACTDue 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 ... Continue reading---
APPENDIX A - [ Total Page(s): 2 ]APPENDIX A ... Continue reading---
APPENDIX C - [ Total Page(s): 1 ]APPENDIX Cen-diseases.trainMalaria is a life-threatening mosquito-borne blood disease caused by a Plasmodium parasite Malaria was eliminated from the U.S. in the early 1950sMalaria is typically spread by mosquitoesMalaria symptoms can be classified into two categoriesMalaria happens when a bite from the female Anopheles mosquito infects the body with PlasmodiumMalaria is a mosquito-borne infectious disease affecting humans and other animals caused by parasitic protozoansMalaria is a mosquito-bor ... Continue reading---
APPENDIX B - [ Total Page(s): 11 ]APPENDIX B ... Continue reading---
CHAPTER ONE - [ Total Page(s): 2 ]CHAPTER ONE1.0 INTRODUCTIONThis chapter introduces the topic of the project work A System for Health Document Classification Using Machine Learning. In this chapter, we will consider the background of the study, statement of the problem, aims and objectives, methodology used to design the system, scope of the study, its significance, definition of terms, and we conclude with the project layout or organization of the project work.1.1 BACKGROUND OF THE STUDYContemporarily, most hospita ... Continue reading---
CHAPTER TWO - [ Total Page(s): 3 ]CHAPTER TWOLITERATURE REVIEW2.0 DOCUMENT CLASSIFICATIONClassification can be divided in two principal phases. The first phase is document representation, and the second phase is classification. The standard document representation used in text classification is the vector space model. The difference of classification systems is in document representation models. The more relevant the representation is, the more relevant the classification will be. The second phase includes learning from tr ... Continue reading---
CHAPTER THREE - [ Total Page(s): 3 ]3.4 SEQUENCE DIAGRAMSequence diagrams are simple subsets of interaction diagrams. They map out sequential events in an engineering or business process in order to streamline activities. Sequence diagrams are used to show how objects interact in a given situation. An important characteristic of a sequence diagram is that time passes from top to bottom: the interaction starts near the top of the diagram and ends at the bottom (i.e. Lower equals Later).3.5 CLASS DIAGRAMSWe begin our OOD ... Continue reading---
CHAPTER FOUR - [ Total Page(s): 5 ]CHAPTER FOUR SYSTEM IMPLEMENTATION4.0 INTRODUCTIONAfter careful requirement gathering, analysis and design, the system is implemented. Implementation involves testing the system with required data and observing the results to see if the system has been properly deigned or if it contains bugs. This is usually done with data which has known results. In this chapter we will implement the system designed.4.1 SYSTEM REQUIREMENTSTo implement the application, the computer on which it will r ... Continue reading---
REFRENCES - [ Total Page(s): 1 ]REFERENCERussell Power, Jay Chen, Trishank Karthik and Lakshminarayanan Subramanian (2018),“Document Classification for Focused Topics†https://cs.nyu.edu/~jchen/publications/aaai4d-power.pdf.Hull D., J. Pedersen, and H. Schutze (1996), “Document routing as statistical classification,†in AAAI Spring Symp. On Machine Learning in Information Access Technical Papers, Palo Alto.Fox C. (1992), “Lexical analysis and stoplist,†in Information Retrieval Data Structur ... Continue reading---
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ABSRACT - [ Total Page(s): 1 ]ABSTRACTDue 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 ... Continue reading---
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CHAPTER FIVE -- [Total Page(s) 1]
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