Knowledge Discovery in Clinical Performance of Cancer Patients

Our goal in this research is to construct predictive models for clinical performance of pancreatic cancer patients. Current predictive model design in medical oncology literature is dominated by linear and logistic regression techniques. We seek to show that novel machine learning methods can perform as well or better than these traditional techniques.We construct these predictive models via a clinical database we have developed for the University of Massachusetts Memorial Hospitalin Worcester, Massachusetts, USA. The database contains retrospective records of 91 patient treatments for pancreatic tumors.Classification and regression prediction targets include patient survival time, ECOG quality of life scores, surgical outcomes,and tumor characteristics. The predictive accuracy of various data mining models is described, and specific models are presented.