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Hastanelerin Gelecekteki Hasta Yoğunluklarının Veri Madenciliği Yöntemleri İle Tahmin Edilmesi

Predicting Future Patient Volumes of The Hospitals By Using Data Mining Methods

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Abstract (2. Language): 
In this study, some techniques of data mining were used to predict the future volumes of patients in a currently active database of a hospital and the results were presented comparatively. Initially, data transfer, filtering and data pre-processing activities were performed in the hospital database. In order to predict the future volumes of patients, different models of exponential smoothing, ARIMA and neural network techniques were generated and the best models of each technique were determined. The best models of each technique then evaluated again to determine the best predictive model. Winters Additive exponential smoothing model, ARIMA(3,1,0)(1,0,0) 12 model, and ANN model trained by using Prune method have yield better results. The results of comparison showed that Winters Additive exponential smoothing model was the best predictive model for this data and this model made the closest predictions to actual values.
Abstract (Original Language): 
Bu çalışmada hali hazırda işleyen bir hastane veritabanında bazı önemli veri madenciliği teknikleri ile hasta yoğunluklarının tahmin edilmesi uygulamaları yapılmış ve sonuçları karşılaştırmalı olarak aktarılmıştır. Kullanılan hastane veritabanında veri transferi, filtreleme ve veri ön-işleme faaliyetleri gerçekleştirilmiş sonrasında da zaman serileri ve yapay sinir ağları teknikleri kullanılarak birçok veri madenciliği tahmin modeli üretilmiştir. Üstel düzgünleştirme, ARIMA ve yapay sinir ağları yöntemleri önce kendi içlerindeki farklı modellerle kıyaslanmış sonrasında da her yöntemin en kestirimci modelleri birbirleriyle kıyaslanarak bu konuda en iyi sonucu veren model tespit edilmeye çalışılmıştır. Üstel düzgünleştirme yöntemlerinden Winters Additive modeli, ARIMA yöntemlerinden ARIMA(3,1,0)(1,0,0) 12 modeli ve yapay sinir ağları yöntemlerinden Prune yöntemi ile elde edilen model en iyi sonuçları vermiştir. Winters Additive üstel düzgünleştirme modeli ise bunlar arasında en kestirimci model olarak öne çıkmış ve gerçekleşen değerlere en yakın tahminleri üretmiştir.
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REFERENCES

References: 

ARULAMPALAM, G. ve BOUZERDOUM, A., “A Generalized Feedforward Neural Network Architecture for Classification and
Regression”, Neural Networks, 16, (2003), 561-568.
BERNARDOS, P. G. ve VOSNIAKOS, G. C., “Optimizing Feedforward Artificial Neural Network Architecture”, Engineering
Applications of Artificial Intelligence, 20, (2007), 365-382.
BERRY, M. J. A. ve LINOFF, G. S., Data Mining Techniques for Marketing, Sales, and Customer Relationship Management (Second
Edition), Wiley Publishing Inc., Indianapolis, Indiana, 2004.
BISHOP, C. M., Neural Networks for Pattern Recognition, Oxford University Press, Great Clarendon Street, Oxford, UK, 2005.
BOWERMAN B. L. ve O’CONNELL, R. T., Forecasting and Time Series: An Applied Approach, Third Edition, Duxbury Thomson
Learning, Pacific Grove, CA, USA, 1993.
BOWERMAN, B. L., O’CONNELL, R. T. ve KOEHLER, A. B., Forecasting, Time Series, and Regression: An Applied Approach,
Fourth Edition, Thomson Brooks/Cole, Belmont, CA, USA, 2005.
BOX, G. E. P. ve JENKINS, G. M., Time Series Analysis: Forecasting and Cotrol, Holden-Day, San Francisco, 1970.
BROHMAN, M. K., “Knowledge Creation Opportunities in the Data Mining Process”, Proceedings of the 39th Hawaii International
Conference on System Sciences, Vol.8, (2006), 1-10.
DELAVARI, N., BEIKZADEH, M. R. ve PHON-AMNUAISUK, S., “Application of Enhanced Analysis Model for Data Mining
Processes in Higher Educational System”, IEEE ITHET 6th Annual International Conference, Juan Dolio, Dominican
Republic, (2005), F4B/1-6.
DUNHAM, M. H., Data Mining: Introductory and Advanced Topics, Prentice-Hall, Upper Saddle River, NJ, USA, 2003.
FAYYAD, U. M., “Data Mining and Knowledge Discovery: Making Sense Out of Data”, IEEE Intelligent Systems, 11(5), (1996), 20 -25.
FAYYAD, U. M., Piatetsky-Shapiro, G. ve Smyth, P., “From Data Mining to Knowledge Discovery in Databases”, Artificial Intelligence
Magazine, Fall, (1996), 37-54.
FU, Y., “Data Mining: Tasks, Techniques and Applications”, IEEE Potentials, 16(4), (1997), 18-20.
GIUDICI, P., Applied Data Mining: Statistical Methods for Business and Industry, John Wiley & Sons, West Sussex, England, 2003.
GOH, S. L. ve MANDIC, D. P., “Recurrent Neural Networks with Trainable Amplitude of Activation Functions”, Neural Networks, 16,
(2003), 1095-1100.
HAN, J. ve KAMBER, M., Data Mining: Concepts and Techniques, 2nd Edition, Morgan Kaufmann Publishers, San Francisco, CA,
USA, 2006.
HAND, D., MANNILA, H. ve SMYTH, P., Principles of Data Mining, The MIT Press, Cambridge, Massachusetts, USA, 2001.
HILL, T., O’CONNOR, M. ve REMUS, W., “Neural Network Models for Time Series Forecasts”, Management Science, 42, (1996),
1082-1092.
HO, S. L., XIE, M. ve GOH, T. N., “A Comperative Study of Neural Network and Box-Jenkins ARIMA Modeling in Time Series
Prediction”, Computers & Industrial Engineering, 42, (2002), 371-375.
IRMAK, S., Veri Madenciliği Yöntemleri ile Sağlık Sektörü Veritabanlarında Bilgi Keşfi: Tanımlayıcı ve Kestirimci Model
Uygulamaları, Akdeniz Üniversitesi S.B.E. Yayımlanmamış Doktora Tezi, 2009.
KADILAR, C., SPSS Uygulamalı Zaman Serileri Analizine Giriş, Bizim Büro Basımevi, Ankara, 2005.
KANTARDZIC, M., Data Mining: Concepts, Models, Methods, and Algorithms, IEEE Press, Hoes Lane, Piscataway, NJ, USA, 2003.
KENDALL, K. E. ve KENDALL, J. E., Systems Analysis and Design, 7/E, Prentice Hall, Upper Saddle River, NJ, USA, 2008.
KIRKUP, L., Data Analysis with Excel: An Introduction for Physical Scientists, Cambridge University Press, Cambridge, UK, 2002.
KOEHLER, A. B., SNYDER, R. D. ve ORD, J. K., “Forecasting Models and Prediction Intervals for the Multiplicative Holt-Winters
Method”, International Journal of Forecasting, 17, (2001), 269-286.
KOHZADI, N., BOYD, M. S., KERMANSHAHI, B. ve KAASTRA, I., “A Comparison of Artificial Neural Network and Time Series
Model for Forecasting Commodity Prices”, Neurocomputing, 10, (1996), 169-181.
LI, Q. ve KHOSLA, R., “Performance Optimization of Data Mining Applications Using a Multi-layered Multi-agent Data Mining
Architecture”, CIMSA 2005 – IEEE International Conference on Computational Intelligence for Measurement Systems and
Applications, Giardini Naxos, Italy, July (2005), 227-231.
MA, L. ve KHORASANI, K., “A New Strategy for Adaptively Constructing Multilayer Feedforward Neural Networks”,
Neurocomputing, 51, (2003), 361-385.
MAIER, H. R. ve DANDY, G. C., “Neural Network Models for Forecasting Univariate Time Series”, Neural Networks World, 6, (1996),
747-772.
IRMAK-KÖKSAL-ASİLKAN
114
MARAKAS, G. M., Modern Data Warehousing, Mining, and Visualization: Core Concepts, Prentice Hall, Upper Saddle River, New
Jersey, USA, 2003.
MICROSOFT (2006), Microsoft Research, Data Mining: Efficient Data Exploration and Modeling,
http://research.microsoft.com/dmx/DataMining, (12.05.2006).
OLARU, C. ve WEHENKEL, L., “Data Mining”, IEEE Computer Applications in Power, 12(3), (1999), 19-25.
ORHUNBILGE, N., Zaman Serileri Analizi Tahmin ve Fiyat İndeksleri, Avcıol Basım Yayın, İstanbul, 1999.
PHAM, D. T., PACKIANATHER, M. S. ve CHARLES, E. Y. A., “A Novel Self-Organised Learning Model with Temporal Coding for
Spiking Neural Networks”, (in Eds. PHAM, D. T., ELDUKHRI, E. E. ve SOROKA, A. J.) Intelligent Production Machines
and Systems, Cardiff University, Manufacturing Engineering Centre, Cardiff, UK., (2006), 307-312.
PRYBUTOK, V. R., YI, J. ve MITCHELL, D., “Comparison of Neural Network Models with ARIMA and Regression Models for
Prediction of Houston’s Daily Maximum Ozone Concentrations”, European Journal of Operational Research, 122, (2000),
31-40.
SHMUELI, G, PATEL, N. R. ve BRUCE, P. C., Data Mining for Business Intelligence: Concepts, Techniques, and Applications in
Microsoft Office Excel with XLMiner, John Wiley & Sons, Hoboken, NJ, USA, 2007.
SPSS (2007a), Clementine11.1 User’s Guide, Integral Solutions Limited, Chicago, IL., 2007.
SPSS (2007b), Clementine11.1 Node Reference, Integral Solutions Limited, Chicago, IL, 2007.
TAN, P.-N., STEINBACH, M. ve KUMAR, V., Introduction to Data Mining, Pearson, Addison-Wesley, Boston, MA, USA, 2006.
TANG, Z. H. ve MACLENNAN, J., Data Mining with SQL Server 2005, Wiley Publishing Inc., Indianapolis, IN, USA, 2005.
TSAI, C. Y. ve TSAI, M. H., “A Dynamic Web Service based Data Mining Process System”, Proceedings of The Fifth International
Conference on Computer and Information Technology (CIT’05), Washington, DC, USA, IEEE Computer Society, (2005),
1033-1039.
TSENG, F. M., YU, H. C. ve TZENG, G. H., “Combining Neural Network Model with Seasonal Time Series ARIMA Model”,
Technological Forecasting & Social Change, 69, (2002), 71-87.
TSETSEKAS, C. A., FERTIS, A. G. ve VENIERIS, I. S., “Dynamic Application Profiles using Neural Networks for Adaptive Quality of
Service Support in the Internet”, Computer Communications, 29, (2006), 2985-2995.
WANG, L. ve FU, X., Data Mining with Computational Intelligence, Springer-Verlag Berlin Heidelberg, Germany, 2005.
WINTERS, P. R., “Forecasting Sales by Exponentially Weighted Moving Averages”, Management Science, 6, (1960), 324 -342.
ZHANG, G. P., Neural Networks in Business Forecasting, Idea Group Publishing, Hershey, PA, 2004.
ZOU, H. F., XIA, G. P., YANG, F. T. ve Wang, H. Y., “An Investigation and Comparison of Artificial Neural Network and Time Series
Models for Chinese Food Grain Price Forecasting”, Neurocomputing, 70, (2007), 2913-2923.
ZUBCOFF, J., PARDILLO, J. ve TRUJILLO, J., “A UML Profile for the Conceptual Modeling of Data-Mining with Time-Series in
Data Warehouse”, Information and Software Technology, 51(6), (2009), 977-992.

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