Graduation Semester and Year

2018

Language

English

Document Type

Thesis

Degree Name

Master of Science in Industrial Engineering

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Aera LeBoulluec

Abstract

Health and social care are areas of concern worldwide nowadays. Chronic diseases such as cancer and social problems such as tobacco consumption are leading risks of deaths in many countries, and preventive efforts are urgently needed to decrease the negative impact that those problems cause. Technology has made available an unprecedent amount of data in the health and social care fields, which scientists are using to achieve a better understanding of many problems that are a burden for the health and social systems globally. Although previous studies have provided approaches to analyze data, more efficient and accurate methods are needed to obtain predictive models with better performance. This thesis employs data science tools to analyze the characteristics of health and social care data and determine the best approaches to improve the accuracy and efficiency of predictive models in the studied fields. Data preprocessing is considered the key action to increase the statistical power of the data and perform valid data analyses in this study. In brief, this thesis present two researches that are focused on enhancing the data analysis by implementing data science techniques to preprocess data.

Keywords

Missing data, Feature selection

Disciplines

Engineering | Operations Research, Systems Engineering and Industrial Engineering

Comments

Degree granted by The University of Texas at Arlington

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