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Sains Malaysiana 43(4)(2014):
567–573
A Hybrid Prognostic Model for Oral Cancer based on Clinicopathologic
(Model Hibrid untuk Prognosis
Kanser Mulut berdasarkan kepada Penanda Klinikopathologi dan Genomik)
SIOW-WEE
CHANG12*,
SAMEEM ABDUL
KAREEM1, AMIR
FEISAL MERICAN
ALJUNID MERICAN2& ROSNAH BINTI ZAIN34
1Department
of Artificial Intelligence, Faculty of Computer Science and Information
Technology University of
Malaya, 50603 Kuala Lumpur, Malaysia
2Bioinformatics
Division, Institute of Biological Science, Faculty of Science, University
of Malaya, 50603 Kuala Lumpur, Malaysia
3Department
of Oral Pathology and Oral Medicine and Periodontology, Faculty
of Dentistry, University of Malaya, 50603 Kuala Lumpur, Malaysia
4Oral
Cancer Research and Coordinating Centre (OCRCC), Faculty of Dentistry,
University of Malaya 50603 Kuala
Lumpur, Malaysia
Received: 11
September 2012/Accepted: 22 July 2013
ABSTRACT
There are very few prognostic
studies that combine both clinicopathologic and genomic data. Most of the
studies use only clinicopathologic factors without taking into consideration
the tumour biology and molecular information, while some studies use genomic
markers or microarray information only without the clinicopathologic parameters.
Thus, these studies may not be able to prognoses a patient effectively.
Previous studies have shown that prognosis results are more accurate when using
both clinicopathologic and genomic data. The objectives of this research were
to apply hybrid artificial intelligent techniques in the prognosis of oral
cancer based on the correlation of clinicopathologic and genomic markers and to
prove that the prognosis is better with both markers. The proposed hybrid model
consisting of two stages, where stage one with Relief F-GA feature selection method to find an optimal feature of subset and
stage two with ANFIS classification
to classify either the patients alive or dead after certain years of diagnosis.
The proposed prognostic model was experimented on two groups of oral cancer
dataset collected locally here in Malaysia, Group 1 with clinicopathologic
markers only and Group 2 with both clinicopathologic and genomic markers. The
results proved that the proposed model with optimum features selected is more
accurate with the use of both clinicopathologic and genomic markers and
outperformed the other methods of artificial neural network, support vector
machine and logistic regression. This prognostic model is feasible to aid the
clinicians in the decision support stage and to identify the high risk markers
to better predict the survival rate for each oral cancer patient.
Keywords: ANFIS;
clinicopathologic; genomic; oral cancer prognosis; Relief F-GA
ABSTRAK
Terdapat kurang
kajian yang memaparkan penyelidikan prognostik yang menggabungkan
kedua-dua klinikopatologi dan genomik. Kebanyakan
kajian hanya menggunakan faktor klinikopatologi tanpa mengambil
kira biologi tumor dan maklumat molekul, manakala beberapa kajian
penyelidik yang lain menggunakan penanda genomik atau maklumat mikroarai
sahaja tanpa menggunakan parameter klinikopatologi. Maka,
kajian ini tidak dapat membuat prognosis pesakit dengan berkesan.
Kajian terdahulu telah menunjukkan bahawa keputusan
prognosis adalah lebih tepat dengan menggunakan kedua-dua klinikopatologi
dan genomik. Tujuan utama kajian ini adalah untuk mengaplikasikan hibrid teknik
kepintaran buatan dalam prognosis kanser mulut berdasarkan kepada
korelasi penanda klinikopatologi dan genomik dan untuk membuktikan
bahawa prognosis adalah lebih baik dengan kedua-dua penanda.
Model hibrid yang dicadangkan terdiri daripada dua peringkat, dengan
peringkat pertama terdiri daripada ReliefF-GA sebagai
kaedah pemilihan untuk mencari ciri optimum subset dan peringkat
dua dengan pengelasan ANFIS untuk mengelaskan
sama ada pesakit hidup atau mati selepas
beberapa tahun didiagnosis. Model ramalan prognostik yang dicadangkan
telah diaplikasikan ke atas dua golongan dataset kanser mulut yang
dikumpulkan di Malaysia, iaitu Kumpulan 1 dengan penanda klinikopatologi
sahaja dan Kumpulan 2 dengan gabungan kedua-dua penanda klinikopatologi
dan genomik. Keputusan yang didapati telah membuktikan bahawa model
yang dicadangkan dengan ciri optimum yang dipilih adalah lebih tepat
dengan kehadiran kedua-dua penanda klinikopatologi dan genomik dan
mengatasi kaedah lain seperti rangkaian
saraf buatan, mesin sokongan vektor dan regresi logistik. Model
prognostik ini boleh dilaksanakan untuk memberi bantuan kepada pakar
klinikal di peringkat membuat sokongan keputusan untuk mengenal
pasti penanda risiko yang tinggi supaya dapat meramalkan kadar
jangka hayat setiap pesakit kanser dengan lebih tepat.
Kata
kunci: ANFIS; genomik;
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*Corresponding author; email: siowwee@um.edu.my
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