Abstract
Abstract
This work contributes to the development of effective statistical methods of big data analysis for type 2 diabetes mellitus (T2DM) risk assessment to be employed in routine clinical practice. The objective of this study to be reached via machine-learning analysis is twofold: investigation of a possible application of biochemical biomarkers for the T2DM risk prediction in case of a limited knowledge of biometrical parameters of an individual, as well as study on the predictive ability of a derived parameter (rate of a biomarker change over time) in T2DM risk prediction. Obtained statistical parameters (AUC, p-value, etc.) justify a relatively high quality of the model. Nevertheless, a further improvement may be addressed through the following avenues: analysis of adding new factors and models, including lifestyle/habits, and genetic parameters.
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