2 April - June 2015
Time Series Forecasting in Anxiety Disorders of Outpatient
Visits using Data Mining
Vatinee Sukmak*,Jaree Thongkam and Jintana Leejongpermpoon
Faculty of Nursing, Mahasarakham University, MahaSarakham, Thailand, 44150.
Faculty of Informatics, Mahasarakham University, MahaSarakham, Thailand, 44150.
Prasrimahabhodi Psychiatric Hospital, Ubonratchathani, Thailand, 34000
*Correspondent author: firstname.lastname@example.org
This study aims to forecast the number of anxiety disorders patients who would be
seeking treatment at an outpatient clinic in 2011 by comparing two Artifiial Neural
Network (ANN) models and selecting the most powerful model. Data were collected
from the Prasrimahabhodi Psychiatric Hospital database. In order to develop a forecasting
model, we used 4 years of data from January 2007 to December 2010 to construct the
demand forecast model, whereas those from the following year (January to December
2011) were used to evaluate the model. Forecasted models were constructed with two ANN
models: Radial Basis Function (RBF) and Multi-Layer Perceptron networks (MLP).
The forecast accuracies for the models were evaluated via Mean Absolute Percentage
Error (MAPE). The RBF was selected as the fial model. The results demonstrated
that monthly anxiety disorders patient visits can be predicted with good accuracy
using the RBF model technique in time series analysis since the MAPE is below 20%.
The majority of patients was female, married, farmers, aged between 40-59 years old and
diagnosed with other anxiety disorders (F41). An average of one hundred and fity patients
of all ages attended each month at outpatient services with the highest being 244 and the
lowest 76. The forecast cases exceeded the actual clinical cases in the 20-39 age groups.
Accurate forecasting of outpatient visits can play a signifiant role in the management of
a health care system.
Keywords: Radial basis function, Multi-layer perceptron networks, Anxiety disorders