Abstract
Breast cancer (BC) seriously threatens women’s health. The establishment of a risk evaluation model cancer is conducive to early screening and prevention of BC. In this article, a new method of the evaluation and the framework of prevention for BC are proposed by synthesizing interval type-2 fuzzy sets (IT2 FSs), two-level fuzzy comprehensive evaluation, and parallel control (Artificial societies, Computational experiments, and Parallel execution, ACP). At the same time, 12 risk factors were selected as indicators to evaluate the risk level of BC, and then, according to the evaluation results, the correspondingly prevention strategies and intervention measures for different risk levels were discussed. Then, the prevention strategies are analyzed and selected in the computational experiment module of the artificial system, and the patient indexes are monitored in real time by the parallel mechanism between the actual and the artificial system, to feedback, adjust, and optimize the prevention scheme in time. This parallel BC prevention process can achieve dynamic closed-loop control effects. A new effective way for BC prevention was presented, which is of great significance for reducing its incidence rate and developing new medical means.
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