Diabetes Risk Data Mining Method Based on Electronic Medical Record Analysis

Liu, Yang; Yu, Zhaoxiang; Yang, Yunlong

doi:10.1155/2021/6678526

Cited by 16 publications

(10 citation statements)

References 37 publications

(65 reference statements)

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“…Data processing can be divided into two parts: data preprocessing and data coding. The implementation process of these two parts is described in the following two parts [14].…”

Section: Realization Of Datamentioning

confidence: 99%

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Chen

2022

Journal of Sensors

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In order to solve the problems of low efficiency, high consumption of human and time resources, and low degree of intelligence in the current financial abnormal data detection system in computerized accounting, this paper proposes a financial abnormal data monitoring and analysis algorithm based on data mining and neural network. The analysis algorithm uses the method of data mining to process the original financial data, remove invalid information, retain valuable information, and standardize the data to solve the problem of large labor and time consumption. Then, neural network-related algorithms are used to identify the anomalies of standardized data, so as to realize the intelligent early warning of financial abnormal data. Compared with the financial audit algorithm in traditional accounting computerization, this algorithm has the advantages of high efficiency, low energy consumption, and high intelligence. The test results show that the classification accuracy of the proposed algorithm for abnormal data can reach more than 90%. It is proved that the algorithm is effective and improves the efficiency at the same time. The classification error rate of the classifier designed in this paper is 22.5%, and the accuracy rate is 77.5%. Both estimated and actual values represent the number of times, and there is no physical unit. The experiment shows that the main reason for the error rate is the delay of the inspection results of financial abnormalities. Through the example analysis, it can be concluded that the proposed intelligent analysis method of financial abnormal data based on deep learning has good effectiveness and accuracy and has a certain practical value.

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“…Data processing can be divided into two parts: data preprocessing and data coding. The implementation process of these two parts is described in the following two parts [14].…”

Section: Realization Of Datamentioning

confidence: 99%

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Chen

2022

Journal of Sensors

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“…e research of the data mining system in China's cloud environment begins with China mobile's data mining based on cloud computing, that is, the construction of a "big cloud" cloud computing platform [12]. At the end of 2008, China Mobile and the Institute of Technology of the Chinese Academy of Sciences jointly developed PDMiner, a data mining software based on cloud computing, which can solve many cloud computing problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of the Data Mining Model in Smart Mobile Education

Wang

Ding

2022

Mobile Information Systems

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In order to solve the problem that education departments urgently need intelligent and efficient information technology to deal with massive data, so as to mine valuable information for management decision-making, an overall scheme framework of education informatization is proposed. The framework takes data mining as a tool, combined with the theoretical knowledge of cloud computing, and takes the student data of a school as an example to verify the practicability of the framework system. The results are as follows: the framework clusters the students of a school. The overall level of students in cluster 1 is high, accounting for 45.07% of the total number. These kinds of students have solid basic theory, strong logical thinking ability, and excellent professional knowledge learning. The overall level of students in cluster 2 is average, accounting for 38.03% of the total number. This kind of students should pay attention to the study of professional courses and the cultivation of professional skills. The rest are students in cluster 3. These kinds of students have weak basic professional knowledge and poor thinking and logic ability. Through the accuracy before and after pruning output from the model, the accuracy before pruning is 87.32% and the accuracy after pruning is 97.37%. The noise data are eliminated in the process of pruning. The framework established in this study provides a certain decision-making basis for education and teaching and explains the feasibility and effectiveness of the application of data mining technology in educational informatization.

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“…Data mining technology refers to classifying and collecting the relevant data involved in a certain range and identifying and detecting the internal relationship and data law between the data. Data mining technology specifically includes three basic parts: data preparation, data law (Exploring the law of data), and data expression [8]. e data mining engine can be designed and classified according to the expected rules of the data set, so as to lay the foundation for the design of the data mining engine and the classification of the potential data set.…”

Section: Concept Of Data Miningmentioning

confidence: 99%

Application of Data Mining in Predictive Analysis of Network Security Model

Bian¹,

Fu²

2022

Security and Communication Networks

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In order to improve the application effect of data mining in the predictive analysis of network security models, this paper starts with the concept of data mining and data sources, then introduces related technologies from data mining technology and security setting collection technology, and finally introduces the computer network security maintenance system based on data mining. The results show that data mining technology plays an important role in the predictive analysis of network security models. The data show that by the end of 2021, the number of security vulnerabilities collected by my country’s information security vulnerability sharing platform has reached 14,871, an increase of 46.6% compared with 2020. Among them, there are 5,567 high-risk vulnerabilities, an increase of about 1,400 vulnerabilities compared with last year. It can be seen that the number of vulnerabilities discovered every year and the number of high-risk vulnerabilities are basically increasing year by year. Therefore, it is recommended to strengthen the promotion of data mining technology in the predictive analysis of network security models, so that it can play a greater role.

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Diabetes Risk Data Mining Method Based on Electronic Medical Record Analysis

Cited by 16 publications

References 37 publications

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network

Application of the Data Mining Model in Smart Mobile Education

Application of Data Mining in Predictive Analysis of Network Security Model

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