The week's pick
Random Articles
Reseach Article
A Comparative Study in Predicting Colon Rectum Cancer using Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) Models
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 44 - Number 9 |
| Year of Publication: 2012 |
| Authors: S. Shenbaga Ezhil, C. Vijayalakshmi |
10.5120/6291-8485
|
S. Shenbaga Ezhil, C. Vijayalakshmi . A Comparative Study in Predicting Colon Rectum Cancer using Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) Models. International Journal of Computer Applications. 44, 9 ( April 2012), 17-22. DOI=10.5120/6291-8485
Abstract
Colon Rectum Cancer is one of the leading cause of cancer deaths worldwide. In this paper, a comparative study is made for the prediction of Colon Rectum Cancer using Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN). For more than twenty decades, Box Jenkin's Auto Regressive Integrated Moving Average (ARIMA) technique is one of the most sophisticated extrapolation method for prediction. It predicts the values in a time series as a linear combination of its own past values, past errors and current and past values by using the concept of time Series. Artificial Neural Network (ANN) is a modern Non Linear Technique used for prediction that involve learning and pattern recognition. Based on the data the model is was modeled is designed by using two techniques for a period of 50 years (from 1960 to 2010) and the Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error(RMSE) are obtained to evaluate the accuracy of the models. Results show that ANN model perform much better than the traditional ARIMA model. Since early detection of cancer is the key to improve survival rate, prediction of Colon Rectum Cancer will greatly facilitate the doctors in the diagnosis of the disease.
References
- M. G. Kendall and A. Stuart, (1966), "The advanced theory of statistics", Vol. 3. Design and Analysis and Time-Series, Charles Griffin & Co. Ltd. , London, United Kingdom.
- G. E. P. Box and G. M. Jenkins (1970), "Time series analysis: Forecasting and control", San Francisco: Holden-Day.
- C. W. J. Granger and P. Newbold, (1986), "Forecasting Economic Time Series" (Academic Press, San Diego).
- G. E. P. Box, G. M. Jenkins and G. C. Riesel, (1994), "Time Series: Analysis: Forecasting and control", Pearson Education, Delhi.
- P. J. Brockwell and R. A. Davis, (1996), "Introduction to time series and forecasting" Springer.
- David B. Fogel, Eugene C. Wasson, Edward M. Boughton, Vincent W. Porto, and Peter J. Angeline, "Linear and Neural Models for Classifying Breast Masses", IEEE transactions on medical imaging, vol. 17, no. 3, June 1998, pp 485?488.
- Heng-Da Cheng, Yui Man Lui, and Rita I. Freimanis "A Novel Approach to Microcalcification Detection Using Fuzzy Logic Technique", IEEE transactions on medical imaging, vol. 17, no. 3, June 1998, pp 442?450.
- Ky Van Ha, "Hierarchical Radial Basis Function Networks", 1998, EEE 1893 PP 1893?1898.
- S. Makridakis, S. C. Wheelwright and R. J. Hyndman, (1998), "Forecasting: methods and applications", New York: John Wiley & Sons.
- Lubomir Hadjiiski, Berkman Sahiner, Heang-Ping Chan, Nicholas Petrick and Mark Helvie, "Classification of Malignant and Benign Masses Based on Hybrid ART2LDA Approach", IEEE transactions on medical imaging, vol. 18, no. 12, December 1999, pp 1178?1187.
- Taio C. S. Santos-Andrk and Anttinio C. Roque da Silva, "A Neural Network Made of a Kohonen's SOM Coupled to a MLP Trained Via Backpropagation for the Diagnosis of Malignant Breast Cancer from Digital Mammograms", 1999 , IEEE pp 3647?3650.
- Azra Alizad, Mostafa Fatemi, Member, Lester E. Wold and James F. Greenleaf, "Performance of Vibro-Acoustography in Detecting Microcalcifications in Excised Human Breast Tissue: A Study of 74 Tissue Samples" IEEE transactions on medical imaging, vol. 23, no. 3, March 2004, pp 307?312.
- Kenneth Revett, Florin Gorunescu, Marina Gorunescu, Elia El-Darzi and Marius Ene, "A Breast Cancer Diagnosis System: A Combined Approach Using Rough Sets and Probabilistic Neural Networks", EUROCON 2005, Serbia & Montenegro, Belgrade, November, 22-24, 2005, pp 1124?1127.
- Patricia Melin and Oscar Castillo: Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing, StudFuzz 172, 85–107 (2005), Springer-Verlag Berlin Heidelberg.
- Xiangchun Xiong, Yangon Kim, Yuncheol Baek, Dae Wong Rhee, Soo-Hong Kim. "Analysis of Breast Cancer Using Data Mining & Statistical Techniques", Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNPD/SAWN'05) 2005 IEEE.
- J. A. Cruz and D. S. Wishart, "Applications of machine learning in cancer prediction and prognosis", Cancer Informatics, Vol. 2, pp. 59?78, 2006.
- Yuanjiao MA, Ziwu WANG, Jeffrey Lian LU, Gang WANG, Peng LI Tianxin MA, Yinfu XIE, Zhijie ZHENG "Extracting Microcalcification Clusters on Mammograms for Early Breast Cancer Detection" Proceedings of the 2006 IEEE International Conference on Information Acquisition August 20-23, 2006, Weihai, Shandong, China, pp 499?504.
- T. Z. Tan, C. Quek, G. S. Ng a, E. Y. K. Ng, "A novel cognitive interpretation of breast cancer thermography with complementary learning fuzzy neural memory structure", Expert Systems with Applications 33 (2007) 652–666.
- Al Mutaz M, Abdalla, Safaai Deris, Nazar Zaki and Doaa M. Ghoneim, "Breast Cancer Detection Based on Statistical Textural Features Classification", 2008, IEEE, pp 728?730.
- Anna N. Karahaliou, Ioannis S. Boniatis, Spyros G. Skiadopoulos, Filippos N. Sakellaropoulos, Nikolaos S. Arikidis, Eleni A. Likaki, George S. Panayiotakis, and Lena I. Costaridou, "Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications", IEEE transactions on information technology in biomedicine, vol. 12, no. 6, November 2008 , pp 731?738.
- Florin gurunescu, Marina gurunescu, Smaranda gurunescu and Elia El-Darzi, "A statistical evaluation of neural computing approaches to predict recurrent events in breast cancer", 4th IEEE International Conference on Intelligent Systems, 2008, pp 38?43.
- Nuryanti Mohd Salleh, Harsa Amylia Mat Sakim and Nor Hayati Othman, "Neural Networks to Evaluate Morphological Features for Breast Cells Classification", IJCSNS International Journal of Computer Science and Network Security, VOL. 8 No. 9, September 2008, pp 51?58.
- Shakti K. Davis, Barry D. Van Veen, Susan C. Hagness and Frederick Kelcz, "Breast Tumor Characterization Based on Ultrawideband Microwave Backscatter", IEEE transactions on biomedical engineering, vol. 55, no. 1, January 2008, pp 237?246.
- Mohammad Sameti, Rabab Kreidieh Ward, Jacqueline Morgan-Parkes and Branko Palcic "Image Feature Extraction in the Last Screening Mammograms Prior to Detection of Breast Cancer" IEEE journal of selected topics in signal processing, vol. 3, no. 1, February 2009, pp 46?52.
- Shlomi Laufer and Boris Rubinsky, "Tissue Characterization with an Electrical Spectroscopy SVM Classifier", IEEE transactions on biomedical engineering, vol. 56, no. 2, February 2009 pp 525?528.
- Shukla Anupam, Tiwari Ritu, Janghel R. R. and Kaur Prabhdeep, Diagnosis of Thyroid Disorders using Artificial Neural Networks on 2009 IEEE International Advance Computing Conference (IACC 2009), Patiala, India, 6–7 March 2009, pp 2722?2726.
Index Terms
Keywords