Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/15880
Title: Data analysis and modeling for engineering and medical applications
Authors: MELISSA ANGELINE SETIAWAN
Keywords: Data analysis, Modeling, Classification, Food product, Cancer, Blood glucose
Issue Date: 4-Jun-2009
Source: MELISSA ANGELINE SETIAWAN (2009-06-04). Data analysis and modeling for engineering and medical applications. ScholarBank@NUS Repository.
Abstract: Multidimensional and multi class problems are found widely not only in chemical and food industry but also in clinical world. The integration between engineering, computing and life sciences has lead to the development of machine learning and data mining technique which facilitate the information extraction contained in dataset. The methods are not only able to identify the important variables characterizing the samples, but are also able to generate accurate and reliable rules which can be used to distinguish classes. In this work, data mining methods are applied for food product quality classification and for early illness detection. The work was then continued by developing a model which is able to capture the dynamics of blood glucose measurement of ICU patients for blood glucose control purposes.
URI: http://scholarbank.nus.edu.sg/handle/10635/15880
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Lissa_Final Thesis.pdf1.16 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

316
checked on Dec 11, 2017

Download(s)

360
checked on Dec 11, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.