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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---
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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---
CHAPTER FIVE -- [Total Page(s) 1]
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CHAPTER FIVE -- [Total Page(s) 1]
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