a probabilistic bayesian classifier approach for breast cancer diagnosis and prognosis

basically, medical diagnosis problems are the most effective component of treatment policies. recently, significant advances have been formed in medical diagnosis fields using data mining techniques. data mining or knowledge discovery is searching large databases to discover patterns and evaluate the probability of next occurrences. in this paper, bayesian classifier is used as a non-linear data mining tool to determine the seriousness of breast cancer. the recorded observations of the fine needle aspiration (fna) tests that are obtained at the university of wisconsin are considered as experimental data set in this research. the tabu search algorithm for structural learning of bayesian classifier and genie simulator for parametric learning of bayesian classifier were used. finally, the obtained results by the proposed model were compared with actual results. the comparison process indicates that seriousness of the disease in 86.18% of cases are guessed very close to the actual values by proposed model.