The Research of Survival Analysis with Data Mining Technology
Hsueh-Fang Chen, Tian‐Shyug Lee · Journal of Data Analysis · 2010
Breast cancer ranks fourth among all cancers in Taiwan, and is a great threat to the lives of Taiwanese women. Breast cancer screening is one of the most effective ways to detect breast cancer early, but the survival rate of screened patients is an important piece of information that is challenging to obtain. The purpose of this study is to use a breast cancer database in combination with data mining technology to construct a five-year survival prediction model for breast cancer patients and to compare its accuracy with traditional statistical methods to provide professional medical teams with more proactive reference information on the survival of patients with breast cancer to aid their treatment decisions.Various classification models are used to screen for the important variables in breast cancer survival rates. Among them, the MARS model achieves optimal classification results with the fewest variables. When an integrated MARS and ANN model is used, the number of dimensions is reduced but the discriminative ability is maintained. The integrated model greatly reduces the calculation time, and the condensation of the data is more easily achieved. A nonparametric analysis using the Friedman's rank test and Wilcoxon signed rank paired test shows a significant difference between the overall discrimination rates of the integrated MARS and BPN model and the other models. Analytic results demonstrate that this integrated model also classifies the survival of patients with breast cancer more accurately, and if applied in practice should help to provide patients with more adequate and timely treatment. The integrated model not only provides medical researchers with some important reference criteria, but also achieves optimal efficacy in terms of data collection and model construction.