Name
Title | ||
|---|---|---|
Predictive Estimates of Risks Associated with Type 2 Diabetes Mellitus on the Basis of Biochemical Biomarkers and Derived Time-Dependent Parameters. |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1089/cmb.2019.0028 | JOURNAL OF COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
big data,machine-learning analysis,risk prediction,T2DM | Risk assessment,Biomarker (medicine),Type 2 Diabetes Mellitus,Bioinformatics,Mathematics | Journal |
Volume | Issue | ISSN |
26.0 | 10 | 1066-5277 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sergey A Solodskikh | 1 | 0 | 0.34 |
| Alexey S Velikorondy | 2 | 0 | 0.34 |
| Vasily N Popov | 3 | 0 | 0.34 |