Version 1
: Received: 30 July 2024 / Approved: 30 July 2024 / Online: 31 July 2024 (08:45:02 CEST)
How to cite:
Valero Sucari, L. ARIMA Models and Parallel Computing Applied to Anemia Diagnosis in Children Under 36 Months in Juninn Region, Peru. Preprints2024, 2024072444. https://doi.org/10.20944/preprints202407.2444.v1
Valero Sucari, L. ARIMA Models and Parallel Computing Applied to Anemia Diagnosis in Children Under 36 Months in Juninn Region, Peru. Preprints 2024, 2024072444. https://doi.org/10.20944/preprints202407.2444.v1
Valero Sucari, L. ARIMA Models and Parallel Computing Applied to Anemia Diagnosis in Children Under 36 Months in Juninn Region, Peru. Preprints2024, 2024072444. https://doi.org/10.20944/preprints202407.2444.v1
APA Style
Valero Sucari, L. (2024). ARIMA Models and Parallel Computing Applied to Anemia Diagnosis in Children Under 36 Months in Juninn Region, Peru. Preprints. https://doi.org/10.20944/preprints202407.2444.v1
Chicago/Turabian Style
Valero Sucari, L. 2024 "ARIMA Models and Parallel Computing Applied to Anemia Diagnosis in Children Under 36 Months in Juninn Region, Peru" Preprints. https://doi.org/10.20944/preprints202407.2444.v1
Abstract
This research explores the application of ARIMA (Autoregressive Integrated Moving Average) models and parallel computing techniques to analyze and forecast anemia diagnoses in children under 36 months in the Junín region of Peru. Using health data from 2023-2024, including insurance type, patient information, diagnosis dates, hemoglobin levels, and treatment details, we develop predictive models to understand trends and patterns in childhood anemia. The study aims to demonstrate the effectiveness of time series analysis and high-performance computing in addressing this critical public health issue. Results indicate improved forecasting accuracy and computational efficiency, potentially aiding in resource allocation and policy development for anemia prevention and treatment programs.
Keywords
ARIMA; parallel computing; anemia; time series analysis; public health; Peru
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
