Predicting the development of T1D and identifying its Key Performance Indicators in children; a case-control study in Saudi Arabia.

ABSTRACT: The increasing incidence of type 1 diabetes (T1D) in children is a growing global concern. It is known that genetic and environmental factors contribute to childhood T1D. An optimal model to predict the development of T1D in children using Key Performance Indicators (KPIs) would aid medical practitioners in developing intervention plans. This paper for the first time has built a model to predict the risk of developing T1D and identify its significant KPIs in children aged (0-14) in Saudi Arabia. Machine learning methods, namely Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Artificial Neural Network have been utilised and compared for their relative performance. Analyses were performed in a population-based case-control study from three Saudi Arabian regions.

SUBMITTER: Alazwari A 

PROVIDER: S-EPMC9977054 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature