Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/14743
Title: | Machine-learning methods for handling missing data in medical questionnaires |
Authors: | Farrugia, Christian |
Keywords: | Numerical analysis -- Data processing Health surveys -- Statistical methods Medical statistics |
Issue Date: | 2016 |
Abstract: | This dissertation will provide web-based software tools to handle the problem of missing data in medical questionnaires. Different methods and techniques to create these tools will be studied and analysed. Different imputation techniques which could be used to address the problem of missing data, would also be analysed. A study of software technologies and language will also be examined in order to be able to develop an application using the most appropriate methods. Already available tools/addons (such as found in MATLAB) will be extended for this application. |
Description: | B.SC.IT(HONS) |
URI: | https://www.um.edu.mt/library/oar//handle/123456789/14743 |
Appears in Collections: | Dissertations - FacICT - 2016 Dissertations - FacICTCIS - 2016 |
Files in This Item:
File | Description | Size | Format | |
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16BITSD015.pdf Restricted Access | 2.19 MB | Adobe PDF | View/Open Request a copy |
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