Date of Award

12-19-2023

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Elizabeth Pierce

Abstract

High-quality food composition data are indispensable for decision making in several health, agricultural, and nutrition-related activities. Analytical data are the most accurate type of food composition data (FCD). Yet, most food composition databases (FCDBs) compiled in Sub-Saharan Africa only contain a small amount of analytical data collected from foods consumed in this region. Most of the data are either borrowed / copied from FCDBs collected in other parts of the world, calculated, imputed, or presumed data. Even with these alternative types, FCDBs in Sub-Saharan Africa have not reached a satisfactory level of completeness, as they still have missing data for several foods and nutrients. Findings from this study include the list of most used FCDBs by key stakeholders in Sub-Saharan Africa. The study has also produced a list of data quality dimensions that are most important to key FCD stakeholders in Sub-Saharan Africa. This list of data quality dimensions has been used to investigate stakeholders’ perceptions of the quality of FCD used in the region. The study then assessed the quality of most used FCDBs in the region through available metadata documentation before it investigated the use of predictive analytics as an approach to solving the missing data problem experienced in most FCDBs in Sub-Saharan Africa.

Download

Included in

Data Science Commons

Share

COinS