Time series forecasting in anxiety disorders of outpatient visits using data mining
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Published:
Jun 1, 2015
Keywords:
Radial basis function Multi-layer perceptron networks Anxiety disorders
Main Article Content
Abstract
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 Artificial 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 final 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 fifty 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 significant role in the management of a health care system.
Article Details
How to Cite
Sukmak, V., Thongkam, J., & Leejongpermpoon, J. (2015). Time series forecasting in anxiety disorders of outpatient visits using data mining. Asia-Pacific Journal of Science and Technology, 20(2), 241–253. https://doi.org/10.14456/kkurj.2015.19
Section
Research Articles
References
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[2] Kessler R, Berglund P, Demler O, Jin R, Merikangas K, Walters E. Lifetime prevalence and age-of-onset distributions of DSM IV disorder in the National Comorbidity Survey Replication. Archives of General Psychiatry. 2005;62:593-600.
[3] Kessler RC, Chiu WT, Demler O, Walters EE. Prevalence, severity and comorbidity of 12 month DSM IV disorders in the National comorbidity survey replication. Archivesof General Psychiatry. 2005;62:617-20.
[4] Wu X, Song Z. Multi-step prediction of chaotic time-series with intermittent failures based on the generalized nonlinear filtering methods. Applied Mathematics and Computation. 2013;219:8584-94.
[5] Bunevicius R, Liaugaudaite V, Peceliuniene J, Raskauskiene N, Bunevicius A, Mickuviene N. Factors affecting the presence of depression, anxiety disorders, and suicidal ideation in patients attending primary health care service in Lithuania. Prim Health Care. 2014;32(1):24-9.
[6] CureResearch.com. Statistics by Country for Anxiety Disorders. 2010 [cited 30 May 2014]; Available from: http://www.curere-search.com/a/anxiety/stats-country.htm
[7] Roy-Byrne P, Davidson K, Kessler R, Asmundson G, Goodwin R, Kubzansky L, et al. Anxiety disorders and comorbid medical illness. General Hospital Psychiatry. 2008;30: 208-25.
[8] Strine T, Mokdad A, Balluz L, Gonzalez O, Crider R, Berry J, et al. Depression and anxiety in the United States: finding from the 2006 behavioral risk factor surveillance system. Psychiatric Services. 2008;59:1383-90.
[9] Sareen J, Jacobi F, Cox B, Belik S, Clara I, Stein M. Disability and poor quality of life associated with comorbid anxiety disorders and physical conditions. Arch Intern Med. 2006;166:2109-16.
[10] Revicki D, Travers K, Wyrwich K, Svedsäter H, Locklear J, Mattera M, et al. Humanistic and economic burden of generalized anxiety disorder in North America and Europe. Affective Disorders. 2011;140:103-12.
[11] Bereza B, Machado M, Einarson T. Systematic review and quality assessment of economic evaluations and quality-of-life studies related to generalized anxiety disorder. Clinical Therapeutics. 2009;31:1279-80.
[12] Olfson M, Marcus S, Wan G, Geissler E. National trends in the outpatient treatment of anxiety disorders. Clinical Psychiatry. 2004;65:1166-73.
[13] Smith R, Larkin G, Southwick S. Trends in U.S. emergency department visits for anxiety-related mental health conditions 1992-2001. Clinical Psychiatry. 2008; 69:286-94.
[14] Larkin G, Claassen C, Emond J, Pelletier A, Camargo C. Trends in U.S. emergency department visits for mental health conditions, 1992 to 2001. Psychiatric Services. 2005; 56: 671-7.
[15] McLean C, Asnaani A, Litz B, Hofmann S. Gender differences in anxiety disorders: prevalence, course of illness, comorbidity and burden of illness. Psychiatr Research. 2011;45(8):1027-35.
[16] Singleton N, Bumpstead R, O’Brien M, Lee A, Meltzer H. Psychiatric morbidity among adults living in private households, 2000. International Review of Psychiatry. 2003;15: 65-73.
[17] Hunt C, ISSAKIDIS C, ANDREWS G. DSM-IV generalized anxiety disorder in the Australian National Survey of Mental Health and Well-Being. Psychological Medicine. 2002;32:649-59.
[18] Hadavandi E, Shavandi H, Ghanbari A, Abbasian-Naghneh S. Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals. Applied Soft Computing. 2012;12:700-11.
[19] Catto J, Linkens D, Abbod M, Chen M, Burton J, Feeley K, et al. Artificial intelligence in predicting bladder cancer outcome: a compar-ison of neuro-fuzzy modeling and artificial neural networks. Clinical Cancer Research. 2003;9:4172-17.
[20] Hadavandi E, Hadavandi E, Shavandi H, Ghanbari A, Abbasian-Naghneh S. Developing a hybrid artificial intelligence model for outpatient visit forcasting in hospitals. Applied Soft Computing. 2012;12:700-11.
[21] Guan P, Huang DS, Zhou BS. Forecasting model for the incidence of hepatitis: A based on artificial neural network. World Journal of Gastroenterology. 2004;10(24):3579-82.
[22] Liu H, Motoda H. Feature extraction construction and selection: a data mining perspective. Massachusetts: Kluwer Academic Publishers; 1998.
[23] Zounemat-kermania M, Kisib O, Rajaeec T. Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff. Applied Soft Computing. 2013;13:4633-44.
[24] Magudeeswaran G, Suganyadevi D. Forecast of Diabetes using Modified Radial basis Functional Neural Networks. the Proceedings on Research Trends in Computer Technologies; 2013;2013;35-9.
[25] Kourentzes N, Barrow DK, Crone SF. Neural network ensemble operators for time series forecasting. Expert Systems with Applications. 2014;41:4235-44.
[26] Yilmaz I, Kaynar O. Multiple regression, ANN [RBF, MLP] and ANFIS models for prediction of swell potential of clayey soils. Expert Systems with Applications. 2011; 38(5):5958–66.
[27] Oludolapo OA, Jimoh AA, Kholopane PA. Comparing performance of MLP and RBF neural network models for predicting South Africa’s energy consumptio. Energy in Southern Africa. 2012;23:40-6.
[28] Poggio T, Girosi F. A Theory of Networks for Approximation and Learning; 1989.
[29] Yu H, Xie T, Paszezynski S. Wilamowski BM Advantages of Radial Basis Function Networks for Dynamics System Design. IEEE Transcations of Industrial Electron-ics; 2011;5438-50.
[30] Witten IH, Frank E, Trigg L, Hall M, Holmes G, Cunningham SJ. Weka: Practical machine learning tools and techniques with java implementations. International Workshop: Emerging Knowledge Engineering and Connectionist-Based Information Systems. 1999;192-6.
[31] Memarian H, Balasun SK. Compar-ison between Multi-Layer Percep-tron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed. Water Resource and Protection. 2012;4:870-6.
[32] Lewis C. Demand Forecasting and Inventory Control. Cornwall England: Woodhead publishing limited; 1997.
[33] Mukhopadhyay SK. Production Planning and control- Text and Cases. 2nd ed. New Delhi, Asoke K. Ghush: Phi learning private limited; 2007.