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.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence