Examining the Factors of Self-Compassion Scale with Canonical Commonality Analysis: Syrian Sample

Ozdemir, Burhanettin; Seef, Nesrin

doi:10.14689/ejer.2017.70.2

Cited by 1 publication

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Strategy Of Diabetes Risk Data Mining Methods Based On Elementioning

confidence: 99%

“…According to the different research purposes, different medical records in the electronic medical records are selected, and different attribute information in the medical records is screened according to different target attributes. After data selection, the research data is determined [9]. e standardized adjustment of the electronic medical record text data includes the sorting of input data, the processing of missing data, and the correction of error information.…”

Section: Strategy Of Diabetes Riskmentioning

confidence: 99%

See 1 more Smart Citation

Diabetes Risk Data Mining Method Based on Electronic Medical Record Analysis

Liu

Yang

2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

In today’s society, the development of information technology is very rapid, and the transmission and sharing of information has become a development trend. The results of data analysis and research are gradually applied to various fields of social development, structured analysis, and research. Data mining of electronic medical records in the medical field is gradually valued by researchers and has become a major work in the medical field. In the course of clinical treatment, electronic medical records are edited, including all personal health and treatment information. This paper mainly introduces the research of diabetes risk data mining method based on electronic medical record analysis and intends to provide some ideas and directions for the research of diabetes risk data mining method. This paper proposes a research strategy of diabetes risk data mining method based on electronic medical record analysis, including data mining and classification rule mining based on electronic medical record analysis, which are used in the research experiment of diabetes risk data mining method based on electronic medical record analysis. The experimental results in this paper show that the average prediction accuracy of the decision tree is 91.21%, and the results of the training set and the test set are similar, indicating that there is no overfitting of the training set.

show abstract

Section: Strategy Of Diabetes Risk Data Mining Methods Based On Elementioning

confidence: 99%

Section: Strategy Of Diabetes Riskmentioning

confidence: 99%