A Hybrid Model to Support the Early Diagnosis of Breast Cancer

AbstractDecision-making is a human behavior aiming at the selection of an alternative from groups of real alternatives. Breast Cancer is top cancer of a woman both in developed and developing the world, furthermore breast cancer is the second most frequent cause of death for women in the United States as well as in Asia. Moreover, the early diagnosis is vital to a treatment with a better chance of success. Multiple variables are involved in the process of diagnosis. This study aims to use a Hybrid model to support the early diagnosis of breast cancer. We have proposed the utilization of a hybrid model structured in methodologies build a Bayesian Network to calculate the condition probability of a given person having breast cancer and to support decision (Multi-Criteria Decision Analysis - MCDA) with the goal to achieve optimal to identify the most influential attributes and have a more accurate result than using Bayesian Network alone