Comparison of the Efficiency of Data Mining Algorithms in Predicting the Diagnosis of Diabetes

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Dekamini Fatemeh; EHSANIFAR MOHAMMAD

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Iranian Journal of Diabetes and Metabolism Year:2021 | Volume:21 | Issue:4 Page(s): 264-275

Download Full-Text

Persian Verion

View:

374

Download:

Cites:

Information Journal Paper

Title

Comparison of the Efficiency of Data Mining Algorithms in Predicting the Diagnosis of Diabetes

Author(s)

Dekamini Fatemeh | EHSANIFAR MOHAMMAD | Issue Writer Certificate

Keywords

Data Mining

Diabetes

Efficiency

Knowledge Discovery

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.

Cites

No record.

References

No record.

Cite

APA: Copy

Dekamini, Fatemeh, & EHSANIFAR, MOHAMMAD. (2021). Comparison of the Efficiency of Data Mining Algorithms in Predicting the Diagnosis of Diabetes. JOURNAL OF DIABETES AND METABOLIC DISORDERS, 21(4 ), 264-275. SID. https://sid.ir/paper/1009047/en

Vancouver: Copy

Dekamini Fatemeh, EHSANIFAR MOHAMMAD. Comparison of the Efficiency of Data Mining Algorithms in Predicting the Diagnosis of Diabetes. JOURNAL OF DIABETES AND METABOLIC DISORDERS[Internet]. 2021;21(4 ):264-275. Available from: https://sid.ir/paper/1009047/en

IEEE: Copy

Fatemeh Dekamini, and MOHAMMAD EHSANIFAR, “Comparison of the Efficiency of Data Mining Algorithms in Predicting the Diagnosis of Diabetes,” JOURNAL OF DIABETES AND METABOLIC DISORDERS, vol. 21, no. 4 , pp. 264–275, 2021, [Online]. Available: https://sid.ir/paper/1009047/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops