Prediction Number of Admissions in the Psychiatric Department of Hospital Universiti Sains Malaysia (HUSM) Using the ARIMA Model

Mariathy Karim and Nur Syuhada Muhammat Pazil
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Mariathy Karim: Mathematical Sciences Studies, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Melaa Branch, Jasin Campus
Nur Syuhada Muhammat Pazil: Mathematical Sciences Studies, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Melaa Branch, Jasin Campus

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 1, 3650-3660

Abstract: In Malaysia, mental health issues are on the rise. Mental health refers to a person’s psychological, emotional, and social well-being, which helps to understand how they deal with stress, make decisions, and connect with others. However, with the current outbreak of COVID-19, which has happened all over the world, including in our country, the trend has been an increase in psychiatric department admissions. Therefore, this study aims to predict the number of patient admissions at the Psychiatrist Department of Hospital Universiti Sains Malaysia (HUSM) using the Auto-Regressive Integrated Moving Average (ARIMA) model. The appropriate model is determined by comparing the measurement errors Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percent Error (MAPE), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Then, the predicted number of patients was calculated using the best model generated. The finding in this study indicates that ARIMA (1,1,3) was the best-fit model for predicting the number of patients. The prediction model is generally reliable, especially in the middle months of the period studied, where the accuracy is consistently high. This strategy might be applied to data from any hospital department with the same trend pattern for further comparison or future perspective. More time series models could be used to forecast the number of patients for future research.

Date: 2025
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