Applied Research On Local Qualitative Factors In The Analysis Of Medical Data

With the rapid development of biomedical engineering, improvement of measurement techniques makes a lot of medical information to be recorded in electronic format, this information includes not only CT imaging, X-rays, the physiological indicators, including the patient’s age, sex, weight, height, past history, etc. With the development of the time,the amount of database information in these hospitals to continue the expansion, to grow exponentially, although the emergence of the database technology makes very easy to store and retrieve such information,it still can not change the phenomenon of the data-rich but knowledge poor. How to take advantage of these valuable data with help of computer to provide a basis for the diagnosis and treatment of disease,how to find the these data behind valuable medical information which become gradually attentions, these have become a hot issue.It might to solve these problems become data mining technology emerged. Data mining techniques are implicit in which unknown information automatically extracted from the database, the extracted information can be expressed as the mode, rule, concept and other forms. Data mining technology already achieved better results in disease diagnosis, medical image analysis,and the analysis of disease-related factors.Clustering is an important technology in data mining, boundary detection is a breakdown of the clustering technology, boundary detection technology provides the possibility of prevention and prediction of disease for medical.This paper research on the existing clustering boundary detection algorithm and achieved the following results:(1) This paper studied the problem for most of clustering boundary algorithm can not be applied to high-dimensional data, it proposed clustering boundary detection algorithm based on local qualitative factor(BRINK), This algorithm uses weighted euclidean distance to solve high dimensional data problem which most of the existing clusters detecting algorithm can not deal with, Firstly employing the local reachability density to determine the local qualitative factors for each object, Then according to the feature of local qualitative factors,the individual find that it is lightly larger than1in boundary points of clusters.At last,we can detect the boundary points with the former two processes,according to the experimental results of integrated data sets and real data sets,this algorithm can detect boundary points in noisy high-dimensional datasets containing clusters of arbitrary shapes, sizes and different densities.(2) Because there is no specialized medical data mining platform, the authors developed a specific data mining decision-making platform for medical data, The platform uses data preprocessing techniques,and then ues BRINK, Band and other clustering and boundary detection algorithm to cluster and detect boundary on real medical data set,according to the experimental results,The platform can effectively completed the goal,and achieve clustering and clustering boundary detection function on real medical set.