On the Identification of Virtual Tumor Markers and Tumor Diagnosis Predictors Using Evolutionary Algorithms

In this chapter we present results of empirical research work done on the data based identification of estimation models for tumor markers and cancer diagnoses: Based on patients' data records including standard blood parameters, tumor markers, and information about the diagnosis of tumors we have trained mathematical models that represent virtual tumor markers and predictors for cancer diagnoses, respectively. We have used a medical database compiled at the Central Laboratory of the General Hospital Linz, Austria, and applied several data based modeling approaches for identifying mathematical models for estimating selected tumor marker values on the basis of routinely available blood values; in detail, estimators for the tumor markers AFP, CA-125, CA15-3, CEA, CYFRA, and PSA have been identified and are discussed here. Furthermore, several data based modeling approaches implemented in HeuristicLab have been applied for identifying estimators for selected cancer diagnoses: Linear regression, k-nearest neighbor learning, artificial neural networks, and support vector machines (all optimized using evolutionary algorithms) as well as genetic programming. The investigated diagnoses of breast cancer, melanoma, and respiratory system cancer can be estimated correctly in up to 81%, 74%, and 91% of the analyzed test cases, respectively; without tumor markers up to 75%, 74%, and 87% of the test samples are correctly estimated, respectively.