COMPARING THE FUNCTIONALITY OF PREDICTING MODELS FOR BREAST CANCER RECURRENCE BASED ON DATA MINING TECHNIQUES

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.

MIRZAKAZEMI ELHAM; GHAMGOSAR NASERI MOHAMMAD

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

Journal Paper

Paper Information

Journal: Journal of Health Information Management Year:2017 | Volume:14 | Issue:4 (56) Page(s): 144-149

Download Full-Text

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

Persian Verion

View:

1,479

Download:

Cites:

Information Journal Paper

Title

COMPARING THE FUNCTIONALITY OF PREDICTING MODELS FOR BREAST CANCER RECURRENCE BASED ON DATA MINING TECHNIQUES

Author(s)

MIRZAKAZEMI ELHAM | GHAMGOSAR NASERI MOHAMMAD | Issue Writer Certificate

Keywords

DATA MININGQ1

RECURRENCEQ2

BREAST CANCERQ1

ALGORITHMQ2

Abstract

Introduction: After applying BREAST CANCER treatment methods, there is a possibility of RECURRENCE of the disease. The aim of the present study was using DATA MINING techniques in order to provide predicting models for BREAST CANCER RECURRENCE.Methods: 18 features of 809 patients were used in the current descriptive study. The study consisted of two phases, preprocessing phase and model learning. Expectation Maximization (EM) and Classification and Regression (C and R) were used for the analysis of the first phase. In order to analyze the second phase, the five ALGORITHM model including Neural Network, C and R, the decision tree ALGORITHM C5.0, Bayes Net, and Support Vector Machine (SVM) was used.Results: The accuracy of the EM and C and R ALGORITHMs was 0.641 and 0.420, respectively, in the preprocessing phase. The accuracy of Neural Network, C and R, the decision tree ALGORITHM C5.0, Bayes Net, and SVM ALGORITHMs was 0.858, 0.865, 0.870, 0.883, and 0.998, respectively, for the model learning phase.Conclusion: According to the findings, the model with the application of EM ALGORITHM in the first phase and SVM ALGORITHM in the second phase had the highest functionality. It was also important in determining the treatment process.

Cites

No record.

References

No record.

Cite

APA: Copy

MIRZAKAZEMI, ELHAM, & GHAMGOSAR NASERI, MOHAMMAD. (2017). COMPARING THE FUNCTIONALITY OF PREDICTING MODELS FOR BREAST CANCER RECURRENCE BASED ON DATA MINING TECHNIQUES. HEALTH INFORMATION MANAGEMENT, 14(4 (56) ), 144-149. SID. https://sid.ir/paper/121775/en

Vancouver: Copy

MIRZAKAZEMI ELHAM, GHAMGOSAR NASERI MOHAMMAD. COMPARING THE FUNCTIONALITY OF PREDICTING MODELS FOR BREAST CANCER RECURRENCE BASED ON DATA MINING TECHNIQUES. HEALTH INFORMATION MANAGEMENT[Internet]. 2017;14(4 (56) ):144-149. Available from: https://sid.ir/paper/121775/en

IEEE: Copy

ELHAM MIRZAKAZEMI, and MOHAMMAD GHAMGOSAR NASERI, “COMPARING THE FUNCTIONALITY OF PREDICTING MODELS FOR BREAST CANCER RECURRENCE BASED ON DATA MINING TECHNIQUES,” HEALTH INFORMATION MANAGEMENT, vol. 14, no. 4 (56) , pp. 144–149, 2017, [Online]. Available: https://sid.ir/paper/121775/en

Related Journal Papers