Ovarian cancer diagnosis with complementary learning fuzzy neural network.
OBJECTIVE
Early detection is paramount to reduce the high death rate of ovarian cancer. Unfortunately, current detection tool is not sensitive. New techniques such as deoxyribonucleic acid (DNA) micro-array and proteomics data are difficult to analyze due to high dimensionality, whereas conventional methods such as blood test are neither sensitive nor specific.


METHODS
Thus, a functional model of human pattern recognition known as complementary learning fuzzy neural network (CLFNN) is proposed to aid existing diagnosis methods. In contrast to conventional computational intelligence methods, CLFNN exploits the lateral inhibition between positive and negative samples. Moreover, it is equipped with autonomous rule generation facility. An example named fuzzy adaptive learning control network with another adaptive resonance theory (FALCON-AART) is used to illustrate the performance of CLFNN.


RESULTS
The confluence of CLFNN-micro-array, CLFNN-blood test, and CLFNN-proteomics demonstrate good sensitivity and specificity in the experiments. The diagnosis decision is accurate and consistent. CLFNN also outperforms most of the conventional methods.


CONCLUSIONS
This research work demonstrates that the confluence of CLFNN-DNA micro-array, CLFNN-blood tests, and CLFNN-proteomic test improves the diagnosis accuracy with higher consistency. CLFNN exhibits good performance in ovarian cancer diagnosis in general. Thus, CLFNN is a promising tool for clinical decision support.
Tan
Tuan Zea
TZ
School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore.
Quek
Chai
C
Ng
Geok See
GS
Razvi
Khalil
K
D000951:Antigens, Neoplasm / Q000097:blood; D014408:Biomarkers, Tumor / Q000032:analysis; D016208:Databases, Factual; D003658:Decision Making, Computer-Assisted; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D017143:Fuzzy Logic; D006403:Hematologic Tests / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D010051:Ovarian Neoplasms / Q000097:blood / Q000175:diagnosis* / Q000235:genetics; D010363:Pattern Recognition, Automated; D040901:Proteomics / Q000706:statistics & numerical data; D015203:Reproducibility of Results
D000951:Antigens, Neoplasm; D014408:Biomarkers, Tumor