Time Series Forecasting of Cardiovascular Mortality: Machine Learning Based on State Economic and Local Medical Data.
Stud Health Technol Inform
; 314: 42-46, 2024 May 23.
Article
en En
| MEDLINE
| ID: mdl-38785001
ABSTRACT
This study focuses on the complex interplay of healthcare, economic factors, and population dynamics, addressing a research gap in regional-level models that integrate diverse features within a temporal framework. Our primary objective is to develop an advanced temporal model for predicting cardiovascular mortality in Russian regions by integrating global and local healthcare features with economic and population dynamics. Utilizing a dataset from the Almazov Center's Department of Mortality Performance Monitoring, covering 94 regions and 752 records from January 1, 2015, to December 31, 2023, our analysis incorporates key parameters such as angioplasty procedures, population morbidity rates, Ischemic Heart Disease (IHD) and Cardiovascular Diseases (CVD) monitoring, and demographic data. Employing XGBoost and a regression model, our methodology ensures the model's robustness and generalizability.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Enfermedades Cardiovasculares
/
Aprendizaje Automático
/
Predicción
Límite:
Humans
País/Región como asunto:
Asia
/
Europa
Idioma:
En
Revista:
Stud Health Technol Inform
Asunto de la revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Año:
2024
Tipo del documento:
Article
País de afiliación:
Rusia
Pais de publicación:
Países Bajos