A model for temperament (Mizaj) recognition based on fuzzy decision tree in traditional Persian medicine

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.

Rezaei Inanlou Roya; SOLEIMANIAN GHAREHCHOPOGH FARHAD; MOJAHEDI MORTEZA

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

Journal Paper

Paper Information

Journal: JOURNAL OF ISLAMIC AND IRANIAN TRADITIONAL MEDICINE Year:2018 | Volume:9 | Issue:3 (35) Page(s): 227-237

Download Full-Text

Persian Verion

View:

1,032

Download:

Cites:

Information Journal Paper

Title

A model for temperament (Mizaj) recognition based on fuzzy decision tree in traditional Persian medicine

Author(s)

Rezaei Inanlou Roya | SOLEIMANIAN GHAREHCHOPOGH FARHAD | MOJAHEDI MORTEZA | Issue Writer Certificate

Keywords

Temperament (Mizaj) ClassificationQ2

Traditional Persian MedicineQ2

Artificial IntelligenceQ2

Fuzzy Decision TreeQ2

Machine LearningQ2

Abstract

Background and purpose: The concept of temperament (Mizaj) is one of the most important concepts in Traditional Persian Medicine. Many prescriptions for maintaining health, diagnosis, and treatment of diseases are based on the specified temperament (Mizaj) of each person that varies from one person to another. Achieving standard methods for determining the temperament (Mizaj) is one of the main priorities of research in Traditional Persian Medicine. The Fuzzy Decision Tree is one of the intelligent algorithms for data classification. The conclusion tree is the process of using specific examples and reaching a general model whose purpose is to learn how to classify the samples. Methods and Materials: In this paper, the data related to temperament (Mizaj) was used for data mining and modeling of the temperament (Mizaj) diagnosis. To this end, the Fuzzy Decision Tree was trained with fuzzy ID3 algorithm. Results: Two trees were made for warmness/coldness and wetness/dryness. The produced rules were evaluated by Traditional Persian Medicine practitioners regarding their clinical application and accuracy. Conclusion: This was the first research in the field of diagnostic intelligence in Traditional Persian Medicine which can be used at the operational level by the experts of the field. Moreover, it can accept other research fields using optimization and other intelligent algorithms and even manipulative diagnostics.

Cites

No record.

References

No record.

Cite

APA: Copy

Rezaei Inanlou, Roya, SOLEIMANIAN GHAREHCHOPOGH, FARHAD, & MOJAHEDI, MORTEZA. (2018). A model for temperament (Mizaj) recognition based on fuzzy decision tree in traditional Persian medicine. JOURNAL OF ISLAMIC AND IRANIAN TRADITIONAL MEDICINE, 9(3 (35) ), 227-237. SID. https://sid.ir/paper/208112/en

Vancouver: Copy

Rezaei Inanlou Roya, SOLEIMANIAN GHAREHCHOPOGH FARHAD, MOJAHEDI MORTEZA. A model for temperament (Mizaj) recognition based on fuzzy decision tree in traditional Persian medicine. JOURNAL OF ISLAMIC AND IRANIAN TRADITIONAL MEDICINE[Internet]. 2018;9(3 (35) ):227-237. Available from: https://sid.ir/paper/208112/en

IEEE: Copy

Roya Rezaei Inanlou, FARHAD SOLEIMANIAN GHAREHCHOPOGH, and MORTEZA MOJAHEDI, “A model for temperament (Mizaj) recognition based on fuzzy decision tree in traditional Persian medicine,” JOURNAL OF ISLAMIC AND IRANIAN TRADITIONAL MEDICINE, vol. 9, no. 3 (35) , pp. 227–237, 2018, [Online]. Available: https://sid.ir/paper/208112/en

Related Journal Papers