Title: Hybrid Metaheuristic Approach for Diagnosing Breast Cancer from the Medical Database                                               

Statistics of standard health organizations shows that the breast cancer is subsequently a critical setback to the human that create more death rates. The medical analysis is mostly established on data received from various sources along with results of medical examination, patient previous records and other different information that physician consider in order to accomplishing a final diagnostic decision making. Numerous studies have conveyed that a large number of people with breast cancer are undiagnosed due to uncertainty of medical data that contains redundant, incomplete, obscure and unpredictable. The intention of this research is to propose a hybrid metaheuristic approach which includes neural network, fuzzy logic techniques and genetic algorithm to diagnose breast cancer and its types. Neural network and fuzzy logic techniques utilized to diagnosing breast cancer and categorize the types. Genetic algorithm applied to compute the best fitness value of evaluating the diagnosing accuracy. The overall diagnosing result from this study scored very high which is 96.43 % accuracy. The innovative hybrid metaheuristic approach can serve as a supportive tool to help medical experts and to teach medical students and nurse for diagnose breast cancer diseases and to increase the human life time.