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Short-Term Prediction in an Emergency Healthcare Unit: Comparison Between ARIMA, ANN, and Logistic Map Models

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  • Andres Eberhard Friedl Ackermann

    (Production and Systems Engineering Graduate Program, Universidade do Vale do Rio dos Sinos, UNISINOS, Av. Unisinos, 950—Cristo Rei, São Leopoldo 93022-000, Brazil)

  • Virginia Fani

    (Department of Industrial Engineering, University of Florence, Viale Morgagni, 40/44, 50134 Florence, Italy)

  • Romeo Bandinelli

    (Department of Industrial Engineering, University of Florence, Viale Morgagni, 40/44, 50134 Florence, Italy)

  • Miguel Afonso Sellitto

    (Production and Systems Engineering Graduate Program, Universidade do Vale do Rio dos Sinos, UNISINOS, Av. Unisinos, 950—Cristo Rei, São Leopoldo 93022-000, Brazil)

Abstract

Emergency departments worldwide face challenges in managing fluctuating patient demand, which is often inadequately addressed by traditional forecasting methods due to the inherent nonlinearities of data. The purpose of this study is to propose a short-term prediction model for daily attendance in a private emergency healthcare unit in southern Brazil. The study employed seven years of historical data to compare the performance of ARIMA, Artificial Neural Networks (ANNs), and the chaotic logistic map model to forecast next-day arrivals in two specialties, general clinic and pediatric. The errors for the general practitioner and the pediatricians of the ARIMA, ANN, and logistic map models were, respectively, [0.31%, 2.54%, 2.17%] and [32.72%, 10.11%, 7.85%], measured by MAPE (mean absolute percentage error). The logistic map ranked second and first place, respectively, providing acceptable results in both cases. The main innovation is the successful application of a chaotic model, specifically the logistic map, exclusively for one-day prediction variables in the management of health and medical services. In particular, for the pediatrician, a most irregular time series, the logistic map provided the better outcome. For professionals, the study offers an accurate tool for optimizing the allocation of human and material resources and supporting daily strategic decisions. For scholars, it opens research avenues, addressing a gap in the body of knowledge on chaotic models that have not yet been extensively explored in healthcare service demand one-day forecasting.

Suggested Citation

  • Andres Eberhard Friedl Ackermann & Virginia Fani & Romeo Bandinelli & Miguel Afonso Sellitto, 2025. "Short-Term Prediction in an Emergency Healthcare Unit: Comparison Between ARIMA, ANN, and Logistic Map Models," Forecasting, MDPI, vol. 7(3), pages 1-16, September.
    • Handle: RePEc:gam:jforec:v:7:y:2025:i:3:p:52-:d:1752531
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    References listed on IDEAS

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