Advancing Cardiovascular Mortality Trend Analysis: A Machine Learning Approach to Predict Future Health Policy Needs.

Autor:	Feretzakis G; School of Science and Technology, Hellenic Open University, Patras, Greece., Theodorakis N; Department of Cardiology, Amalia Fleming General Hospital, Athens, Greece.; 65+ Clinic, Amalia Fleming General Hospital, Athens, Greece.; School of Medicine, National and Kapodistrian University of Athens, Athens, Greece., Vamvakou G; Department of Cardiology, Amalia Fleming General Hospital, Athens, Greece.; 65+ Clinic, Amalia Fleming General Hospital, Athens, Greece., Hitas C; Department of Cardiology, Amalia Fleming General Hospital, Athens, Greece.; 65+ Clinic, Amalia Fleming General Hospital, Athens, Greece., Anagnostou D; Department of Cardiology, Amalia Fleming General Hospital, Athens, Greece.; 65+ Clinic, Amalia Fleming General Hospital, Athens, Greece., Kalantzi S; 65+ Clinic, Amalia Fleming General Hospital, Athens, Greece.; Department of Internal Medicine, Amalia Fleming General Hospital, Athens, Greece., Spyridaki A; 65+ Clinic, Amalia Fleming General Hospital, Athens, Greece.; Department of Internal Medicine, Amalia Fleming General Hospital, Athens, Greece., Kollia Z; 65+ Clinic, Amalia Fleming General Hospital, Athens, Greece., Christodoulou M; 65+ Clinic, Amalia Fleming General Hospital, Athens, Greece., Kalles D; School of Science and Technology, Hellenic Open University, Patras, Greece., Gkontzis AF; School of Science and Technology, Hellenic Open University, Patras, Greece., Verykios VS; School of Science and Technology, Hellenic Open University, Patras, Greece., Nikolaou M; Department of Cardiology, Amalia Fleming General Hospital, Athens, Greece.; 65+ Clinic, Amalia Fleming General Hospital, Athens, Greece.
Jazyk:	angličtina
Zdroj:	Studies in health technology and informatics [Stud Health Technol Inform] 2024 Aug 22; Vol. 316, pp. 868-872.
DOI:	10.3233/SHTI240549
Abstrakt:	This study investigates the forecasting of cardiovascular mortality trends in Greece's elderly population. Utilizing mortality data from 2001 to 2020, we employ two forecasting models: the Autoregressive Integrated Moving Average (ARIMA) and Facebook's Prophet model. Our study evaluates the efficacy of these models in predicting cardiovascular mortality trends over 2020-2030. The ARIMA model showcased predictive accuracy for the general and male population within the 65-79 age group, whereas the Prophet model provided better forecasts for females in the same age bracket. Our findings emphasize the need for adaptive forecasting tools that accommodate demographic-specific characteristics and highlight the role of advanced statistical methods in health policy planning.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu