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Prediction Model for H1N1 Disease

Ling, Amy Mei Yin (2011) Prediction Model for H1N1 Disease. Masters thesis, Universiti Utara Malaysia.

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Abstract

This research has used the H1N1 disease based on the data collected from outpatient clinics (private and public sectors) across Hong Kong with influenza like illness. The objective of this project is to develop a prediction model of H1N1 disease using Multilayer Perceptron. The experiment using WEKA machine learning tool produced the best parameter's values for the datasets. The General Methodology of Design Research (GMDR) and Knowledge Discovery in Databases (KDD) has been used throughout the study as a guideline. Prediction model for H1N1 disease using MLP has been generated and MLP has performs the good result where the value of accuracy for the H1N1 disease is 88.57%.

Item Type: Thesis (Masters)
Uncontrolled Keywords: H1N1 disease, Multilayer Perceptron, Accuracy’s values
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: College of Arts and Sciences (CAS)
Depositing User: Mr Husni Ismail
Date Deposited: 26 Feb 2012 01:09
Last Modified: 27 Apr 2016 04:33
URI: http://etd.uum.edu.my/id/eprint/2737

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