Statistical Methods and Artificial Intelligence Deep Learning to Identify the Risk Factors of Colorectal Cancer
We also want to rank the risk factor and their interactions according to their percent contribution to colorectal cancer. This helps us to find significantly contributing risk factors. We also going to perform surface response analysis to identify the behavior of the risk factor and its interactions this will minimize colorectal cancer. If possible, we will identify the probability distribution of survival time and shape the parametric and nonparametric models. These statistical models could provide crucial clues about colorectal cancer and help to make a decision about the disease and implement prevention.
1. We are going to utilize artificial intelligence deep learning and advance statistical methods to identify the individual risk factors and their interaction that causes colorectal cancer.
2. We want to rank the risk factor and their interactions according to their percent contribution to colorectal cancer.
3. We are going to perform surface response analysis to identify the behavior of the risk factor and its interactions.
Dr. Chris P. Tsokos (Ph.D. Supervisor)
Distinguished University Professor of Mathematics and Statistics
Department of Mathematics and Statistics, University of South Florida
4202 E. Fowler Ave., CMC 366
Tampa, Florida 33620
Tel. (813) 974-9734
Fax: (813) 974-2700