Showing 2 results for Knowledge Discovery
Aboozar Ramezani, Leila Shahmoradi, Fereydoon Azadeh, Fatemeh Sheikhshoaei, Rasha Atlasi, Nazli Namazi, Bagher Larijani,
Volume 20, Issue 1 (1-2021)
Abstract
Background: A key aspect of Scientific collaboration increases scientific productivity. This study aimed to draw up a scientific collaboration network of the Endocrinology and Metabolism Research Institute (EMRI) at Tehran University of Medical Sciences.
Methods: A Descriptive Cross-Sectional Study was conducted by the Scientometrics method. Data collection from the Scopus and Web of Science Core collection databases between 2002 until 30 October 2020. MS-Excel, HistCite, VOSviewer, and ScientoPy were used for descriptive statistics and data analysis.
Results: A total of 4190 records with the affiliation of the EMRI are indexed in two international databases. All of the records received a sum of 89480 citations. The EMRI Researchers were published in 1118 journals. The annual growth rate of publication and citation of the scientific output of the EMRI was 20.3% and 22.7%, respectively. A total of 17662 authors from 186 countries participated in the publication. The co-authorship pattern shows. The next section of the Study was classified and visualized based on authorship (institutes and country of affiliation), keywords (co-occurrence and trend).
Conclusion: Overall, these results indicate that the pattern of collaborations in the authorships' articles increases the flow of knowledge among the institute's researchers as a result of international collaborations, interaction with leading countries, and interdisciplinary collaborations. To develop a full picture of co-authorship, additional studies will need a comprehensive picture of network cooperation to analyze the situation with other social network analysis indicators.
Fatemeh Dekamini, Mohammad Ehsanifar,
Volume 21, Issue 4 (10-2021)
Abstract
Background: Diabetes is one of the major health problems in Iran and about 4.6 million adults suffer from this disease. Poor diagnosis of this disease has caused half of this number to be unaware of their disease. In recent years, along with the use of computers in data analysis and storage, the volume and complexity of data has increased dramatically.
Methods: In health organizations, data play an essential role in the value of the organization. Therefore, data mining has become one of the most widely used processes in the field of health and disease diagnosis. In this study, the information of 768 laboratory clients in Tehran was kept confidential and the opinions of experts were used to identify the variables affecting the incidence of diabetes.
Results: The findings indicate the study of 5 algorithms on the presented data, which by implementing 5 data mining algorithms J48, Bayes, Beginning, Cohen and simple clustering to classify the data, the efficiency of these algorithms in terms of speed and accuracy in calculations was evaluated.
Conclusion: The data set for classification is the database of a laboratory, which includes 768 samples with 9 characteristics. Finally, J48 algorithm is recommended for data mining of diabetes due to high speed, acceptable accuracy and lack of sensitivity to raw data.
