 |
 |
 |
 |
 |
 |
Determining the Critical Success Factors of Oral
Cancer Susceptibility
Prediction in Malaysia Using Fuzzy Models
(Menentukan Faktor Kejayaan
Kritikal dalam Peramalan Kecenderungan Terjadinya
Kanser Mulut di Malaysia
Menggunakan Model Kabur)
Rosma Mohd Dom
Department of Mathematics, Faculty
of Computer & Mathematical Sciences, Universiti Teknologi MARA , 40450 Shah
Alam, Selangor D.E. Malaysia
Basir Abidin
Director, Foundation in
Science , Cyberjaya University College of Medical Sciences, Unit No 2, Street
Mall 2, 63000 Cyberjaya, Selangor D.E. Malaysia
Sameem Abdul Kareem
Faculty of Computer Science
& Information Teknology, University of Malaya, 50603 Kuala Lumpur
Malaysia
Siti Mazlipah Ismail
Oral Cancer Research &
Coordinating Center, Faculty of Dentistry, Universiti of Malaya
50603 Kuala Lumpur, Malaysia
Norzaidi Mohd Daud*
Faculty of Business
Management/Accounting Research Institute/Institute of Business Excellence
Universiti Teknologi MARA, 40450
Shah Alam, Selangor, Malaysia
Received: 3 February 2010 / Accepted: 18
November 2011
ABSTRACT
The aim of the study was to determine the
success factors of oral cancer susceptibility prediction using fuzzy models.
Three fuzzy prediction models including fuzzy logic, fuzzy neural network and
fuzzy linear regression models were constructed and applied to a Malaysian oral
cancer data set for cancer susceptibility prediction. The three models’
prediction performances were evaluated and compared. All the three fuzzy models
were found to have 64% prediction accuracies for 1-input and 2-input predictor
sets. However, when the number of input predictor set was increased to 3-input
and 4-input, both fuzzy neural networks’ and fuzzy linear regression’s prediction
accuracies increased to 80%, while fuzzy logic prediction accuracy remains at
64%. Fuzzy linear regression model was found to have the capability of
quantifying the relationships between input predictors and the predicted
outcomes and also suitable for small sample size. Fuzzy neural network model on
the other hand, handles ambiguous relationship between variables well but lacks
the ability to describe input-output association. The third model, fuzzy logic,
is easy to construct but highly dependent on human expert-input. The outcome of
this study is a computer-based prediction tool which can be used in cancer
screening programs.
Keywords: Fuzzy logic; fuzzy neural
networks; fuzzy regression; oral cancer; prediction performance
ABSTRAK
Matlamat kajian ini adalah untuk mengenal
pasti faktor kejayaan dalam menentukan kecenderungan terjadinya kanser mulut
menggunakan model kabur. Tiga model kabur termasuk model mantik kabur,
rangkaian neuro-kabur dan regresi kabur telah dibangun dan diaplikasikan ke
atas data kanser mulut di Malaysia bagi tujuan menentukan kemungkinan
terjadinya kanser. Kejituan ramalan terjadinya kanser mulut untuk ketiga-tiga
model diukur dan dibandingkan. Ketiga-tiga model kabur didapati memberikan 64%
kejituan ramalan semasa diuji menggunakan satu dan dua faktor penentu. Walau
bagaimanapun, apabila bilangan faktor penentu ditambah kepada tiga dan empat,
kejituan ramalan model rangkaian neuro-kabur dan regresi kabur meningkat kepada
80% tetapi kejituan ramalan model mantik kabur kekal di paras 64%. Model
regresi kabur mampu mengukur kuantiti hubung kait di antara faktor penentu
dengan hasilannya dan ia juga sesuai digunakan untuk sampel yang kecil. Model
rangkaian neuro-kabur mengambil kira hubungan ketidaktentuan di antara faktor
penentu dan hasilannya tetapi tidak mempunyai keupayaan mengukur kuantiti
hubung kait di antara mereka. Model ketiga iaitu mantik kabur pula mudah untuk
dibangunkan tetapi terlalu bergantung kepada input pakar. Kajian ini
menghasilkan satu alat pengukur berasaskan komputer yang boleh digunakan bagi
tujuan saringan pesakit kanser.
Kata
kunci: Kanser mulut; kejituan ramalan; mantik kabur; rangkaian neuro-kabur;
regresi kabur
REFERENCES
Abd-Kareem, S. 2002. Application of
artificial neural network for the prognosis of nasopharyngeal carcinoma.
Computer Science Department. University of Malaya, Kuala Lumpur, PhD thesis
(unpublished).
Castellano, G. & C., Fanelli, A.
2005. Knowledge discovery by a neuro-fuzzy modeling framework. Fuzzy Sets
and Systems 149: 187–207.
Coppin, B. 2004. Artificial
Intelligence Illuminated. Sudbury, Massachusetts: Jones & Barllett
Publishers.
Dreiseitl, S. & Ohno-Machado, L.
2002. Logistic regression and artificial neural network classification models:
a methodology review. Jounal of Biomedical Informatics 35: 352-359.
Gorzalczany, M & Piasta Z. 1999.
Neuro-fuzzy approach versus rough-set inspired methodology for Intelligent
decision support. Information Sciences 120(1): 45-48.
Jang, R. 1993. ANFIS: Adaptive-
Network-Based Fuzzy Inference System. IEEE Trans. On Systems, Man and
Cybernetics 23(3): 665-685.
Kononenko, I. 2000. Machine learning for
medical diagnosis: History, state of the art and perspective. Artificial
Intelligence in Medicine 23(1): 89-109.
Lasko, T., Bhagwat, J., Zou, K., Machado,
L. 2005. The use of receiver operating characteristic curves in biomedical
informatics. Jounal of Biomedical Informatics 38: 404-415.
Mohan, J. & Yaacob, R. 2004. The
Malaysian Telehealth Flagship Application: A national approach to health data
protection and utilisation and consumer rights. International Journal of
Medical Informatics 73: 217-227.
Mohd Dom, R. 2009. A fuzzy regression
model for the prediction of oral cancer susceptibility.Computer Science
Department, University of Malaya, Kuala Lumpur, PhD Thesis (unpublished).
Moraga, C. 2000. Neuro-fuzzy modeling
with standard feedforward neural networks. European Symposium on Intelligent
Techniques Proceedings. Aachen, Germany.
Muzio, L., D’Angelo, M., Procaccini, M.,
Bambini, F., Calvino, F., Florena, AM., Franco, V., Giovanelli, L., Ammatuna,
P. And Campisi, G. 2005. Expression of cell cycle markers and human papilloma
virus infection in oral squamous cell carcinoma: Use of fuzzy neural networks.
Int. J. Cancer 115: 717- 723.
Nasrabadi, M. & Nasrabadi, E. 2004. A
mathematical-programming approach to fuzzy linear regression analysis.
Applied Mathematics and Computation 155: 873-881.
Savic, D. & Pedrycz, W. 1991.
Evaluation of fuzzy linear regression models. Fuzzy Sets and Systems 39(1):
51-63.
Speight, P., Elliott, A., Jullien, J.,
Downer & M., Zakzrewska, J. 1995. The use of artificial intelligence to
identify people at risk of oral cancer and precancer. British Dental Journal 179: 382-387.
Tanaka, H. & H. Lee 1998. Interval
Regression Analysis by Quadratic Programming Approach. IEEE Transactions on
Fuzzy Systems 6(4):
Wang, H. & Tsaur, R. 2000. Insight of
a fuzzy regression model. Fuzzy set and systems 112: 355-369.
Zadeh, L. A, 1965. Fuzzy Sets. Information
and Control 8: 338-353.
Zain, R. 2001. Cultural and dietary risk
factors of oral cancer and pre-cancer – A brief overview. Oral
Oncology 37:205-210.
*Corresponding author;
email: zaidiuitm2000@yahoo.com
|
|||
|
