Knowledge discovery for health risk prediction

Pham, Thuan ORCID: https://orcid.org/0000-0001-7433-858X (2020) Knowledge discovery for health risk prediction. [Thesis (PhD/Research)]

[img]
Preview
Text (Whole Thesis)
Thuan Pham_Thesis_Knowledge Discovery For Health Risk Prediction.pdf

Download (1MB) | Preview

Abstract

Improving the accuracy of the diagnosis of disease can help to increase the quality of healthcare. Many researchers have developed classification models to support healthcare practitioners to make accurate diagnoses, avoiding the need to rely on their experience base diagnose diseases. However, these models are currently based on datasets collected from healthcare data including medical history. As a result, the reliability and accuracy of predicting results of the diagnosis, are limited.

Following the goal of improving the accuracy of health risk prediction, this thesis concentrates on the classification of tasks through mining healthcare data. The study suggests several frameworks and algorithms to develop classification models. In addition, challenges of extracting useful information and processing data noise from the real dataset are addressed as a way of learning models. Classification models are developed based on well-proven medical data sources. By using medical evidence, the study aims to improve the accuracy of classification for health risk prediction.

The first contribution of this thesis is an innovation of building a binary classification model to predict patients’ risks. The second contribution of this dissertation is to build a medical knowledge base to support classification models for improving the reliability and accuracy of the model. The third significant contribution of the thesis provides a framework for building a predictive model within multiple diseases.


Statistics for USQ ePrint 39851
Statistics for this ePrint Item
Item Type: Thesis (PhD/Research)
Item Status: Live Archive
Additional Information: Doctor of Philosophy (PhD) thesis.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Supervisors: Tao, Xiahui; Zhang, Ji; Yong, Jianming
Date Deposited: 08 Oct 2020 23:09
Last Modified: 06 Oct 2021 22:05
Uncontrolled Keywords: heterogeneous information graph, data mining, healthcare, knowledge discovery
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
08 Information and Computing Sciences > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0806 Information Systems > 080605 Decision Support and Group Support Systems
08 Information and Computing Sciences > 0807 Library and Information Studies > 080702 Health Informatics
Fields of Research (2020): 42 HEALTH SCIENCES > 4203 Health services and systems > 420308 Health informatics and information systems
46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460902 Decision support and group support systems
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460308 Pattern recognition
46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery
Identification Number or DOI: doi:10.26192/jj7h-9231
URI: http://eprints.usq.edu.au/id/eprint/39851

Actions (login required)

View Item Archive Repository Staff Only