A new method to help diagnose cancers for small sample size

Authors:
Der-Chiang Li;Hung-Chang Hsu;Tung-I Tsai;Te-Jung Lu;Susan C. Hu
Affiliations:
Department of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan;Department of Biomedical Engineering, Chimei Medical Center, Tainan, Taiwan;Institute of Business and Management, Tainan Woman's College of Arts and Technology, Tainan 710, Taiwan;College of Medicine, National Cheng Kung University, Tainan, Taiwan;Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2007

Citing 0
Cited 9

A non-parametric learning algorithm for small manufacturing data sets

Expert Systems with Applications: An International Journal
Similarity classifier in diagnosis of bladder cancer

Computer Methods and Programs in Biomedicine
Utilization of virtual samples to facilitate cancer identification for DNA microarray data in the early stages of an investigation

Information Sciences: an International Journal
Incorporating statistical and neural network approaches for student course satisfaction analysis and prediction

Expert Systems with Applications: An International Journal
A class possibility based kernel to increase classification accuracy for small data sets using support vector machines

Expert Systems with Applications: An International Journal
Neural networks and other machine learning methods in cancer research

IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Short communication: Diagnosis of bladder cancers with small sample size via feature selection

Expert Systems with Applications: An International Journal
A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets

Artificial Intelligence in Medicine
Prediction of death rate of breast cancer induced from average microelement absorption with neural network

LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation

Quantified Score

Hi-index	12.06

Visualization

Abstract

For many years, scientists have engaged in profiling altered genes to help diagnose related cancers. However, the size of the sample to develop a new profile of cancer genes in the beginning stage is usually small because of costly procedure. Researchers are often disturbed by the analytical method because there has been no effective technique to deal with such small sample size situations in cancer genes diagnosis. The purpose of the study was to employ a new method, mega-trend-diffusion technique, to improve the accuracy of gene diagnosis for bladder cancer on a very limited number of samples. The modeling results showed that when the number of training data increased, the learning accuracy of the bladder cancer diagnosis was enhanced stably, from 82% to 100%. Compared with traditional methods, this study provides a new approach of a reliable model for small dataset analysis. Although the study treats bladder cancer as an example, it is believed that the findings can be generalized to other diseases with limited sample size.