Research And Implementation Of Birth Defect Early Warning System Based On Association Rules

The incidence of birth defect in China has increased year by year, which threatens human sustainable development and social economic development. Association rules mining, one of data mining methods can find the pathogenic factors by mining the medical data, and then prevent the birth defect. But traditional algorithms of association rules mining have disadvantages of time-consuming and generate redundant rules, which cannot be used to mine the distributed and numeric medical data directly. In view of above two challenges, this paper does the exploratory research about the association rules mining methods of medical data. This paper topic from "Eleventh Five-Year" National Science and Technology Support Project "safe and reliable reproductive health services, telecom operation support system for key technologies", solve the problem of how to mine the factors related with birth defect from1.6million family archives collected by the project, and then achieve the goal of early warning.The work of this paper reflected in the following aspects:1. Research the knowledge of association rules mining, including basic concepts and types. Then focused research and compare the classical algorithms Apriori and FP-growth.2. Propose a new algorithm (ACARMT) which use the constraints based on the interests of users after research exist algorithm.3. In view of the characteristics of medical data, design a data preprocessing model. This model which implements the integration of distributed data and define the data transfer rules to transfer the source data to the Intermediate data which can use the algorithm to mine association rules. This solves the problem of cannot mine association rules in medical data.4. Based on the new algorithm and new model, design and implement a birth defect early warning system to mine the factors lead to birth defect and give early warning to suspicious archives.The main contribution of the paper is to propose a constrained association rules mining algorithm ACARMT which improve the mining efficiency and results’ pertinence, and to design a data preprocessing model which makes the mass medical data can use the new algorithm to mine the association rules. Finally, Application of the ACARMT and data preprocessing model in designing and implement of birth defect early warning system to verify the effectiveness of the algorithm and model and realize the early warning by mining the association rules in1.6million family archives collected by platform of national pre-pregnancy information management.