Medical insurance plays role in the wellbeing of population. However, the difficulty with the amount addition of participants increases dynamically. Especially, the increase of organizations including in this domain makes optimization of medical insurance complex. Thus, it is necessary to find the rule based on the information provided by the related to data as supplementary to support decision making. In order to finish this task, Neural Network, proved its robustness in data analysis, is included in the proposed data management frame. Input and cost as the most important attributes of medical insurance are set as target features. Then, the relationship between these two attributes must be considered. A skipping window is defined to adjust the proportion of the target features in training and testing stages. NN network with double direction of time window for medical insurance is given to foreseeing target attributes. Based on the simulation result, the largest average accuracy is 0.8333.
It is necessary to build effectiveness infectious disease analysis methodology to avoid a large spreading of the disease. The factors playing role in epidemic come from different domain; moreover, their relationship is complex. Thus, it is very hard to mine the rule by single analysis. In this work, a total review is done to analyze the infected in the unit of years, which can provide a foundation to conclude the infectious rule. In order to finish this goal, Part Heuristic K-means based on Improved Grey Correlation Analysis is proposed. It uses improved grey correlation analysis to recognize the relevance among different diseases which has ability to guide the weight. Then, the year is partitioned into clusters based on distance function. It is found that the proportion of three degrees is respectively 21.4%, 28.6%, and 50%; the maximum of relevance is 0.888.
COVID-19 plays role in every part of the world; especially, it does harm to lives of people. Thus, COVID-19 sounds the alarm that is very important to build an effective mechanism to help prevent pandemic disease. In this work, dynamic network based on status value is built, which aims to help simulate the added danger level by the addition of infected people or close contacts. First, each node of this network is labelled with different kinds of status which has special value to show its danger degree. Then, the weight of the network represents the relationship of nodes; with the value of each node, average length and average spread of danger level is calculated based on the accumulation of dynamic weight. Thus, epidemic speed and scope of the infectious disease can be simulated. Moreover, the experiments compared to other networks have verified the effectiveness of our model.
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