1. Advancing Cardiovascular Mortality Trend Analysis: A Machine Learning Approach to Predict Future Health Policy Needs.
- Author
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FERETZAKIS, Georgios, THEODORAKIS, Nikolaos, VAMVAKOU, Georgia, HITAS, Christos, ANAGNOSTOU, Dimitrios, KALANTZI, Sofia, SPYRIDAKI, Aikaterini, Kollia, Zoi, Christodoulou, Michalitsa, KALLES, Dimitris, GKONTZIS, Andreas F., VERYKIOS, Vassilios S., and NIKOLAOU, Maria
- Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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