Research output: Contribution to journal › Conference article › peer-review
Infection with tropical parasitic diseases, according to WHO, has a huge impact on the health of more than 40 million people worldwide and is the second leading cause of immunodeficiency. The number of infections is influenced by many factors - climatic, demographic, vegetation cover and a number of others. The article presents a study and an assessment of the degree of influence of each of these factors, as well as a comparison of the quality of forecasting by separate methods of geo-informational analysis and machine learning and the possibility of their ensemble.
| Translated title of the contribution | Forecasting the distribution of diseases in tropical zones using machine learning methods |
|---|---|
| Original language | Russian |
| Pages (from-to) | 371-376 |
| Number of pages | 6 |
| Journal | CEUR Workshop Proceedings |
| Volume | 2534 |
| State | Published - 12 Jan 2020 |
| Event | 2019 All-Russian Conference "Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes", SDM 2019 - Berdsk, Russian Federation Duration: 26 Aug 2019 → 30 Aug 2019 |
ID: 76310193