Research Article

Estimation of Factors Related to Premenstrual Syndrome in Female Students by Using Artificial Neural Network Model

Volume: 5 Number: 2 December 31, 2020
EN

Estimation of Factors Related to Premenstrual Syndrome in Female Students by Using Artificial Neural Network Model

Abstract

Objective: Premenstrual syndrome is a disorder with psychological and physical symptoms in almost every cycle, and it concerns almost 90% of women of reproductive age. In recent years, studies investigating the relationship between premenstrual syndrome and vitamin D, trace elements, and lymphocyte/platelet ratios have been conducted. In this study, the factors associated with PMS in female students were tried to be determined by using an artificial neural network (ANN) model. Materials and Methods: This study was conducted on female students at Inonu University Faculty of Medicine and Health Sciences, between 01 May and 30 June 2019. Demographic characteristics and menstruation histories of 860 female students were collected and recorded for the study. A multi-layer perceptron artificial neural network model was used to determine the factors associated with premenstrual syndrome. The performance of the model is determined by the accuracy rate and the area under the process characteristic curve. Results: Correct classification rates of the created multi-layer perceptual neural network model for premenstrual syndrome were calculated to be 63.2% in the training data set and 63.0% in the test data set. Considering the importance values of the variables; it was found that the duration of active internet use (phone, tablet, computer) was the most influential factor on premenstrual syndrome and the economic status of a student was the least ,influential factor. Conclusion: According to the findings of the designed artificial neural network model, the three most important factors related to premenstrual syndrome were determined to be the duration of active Internet use, present age and age of menarche. Given the high prevalence of PMS, the uncertainty of etiology, and its potential to affect a woman's lifestyle; the use of artificial intelligence models with larger sample size and including different factors is recommended.

Keywords

References

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Details

Primary Language

English

Subjects

Electrical Engineering

Journal Section

Research Article

Publication Date

December 31, 2020

Submission Date

October 29, 2020

Acceptance Date

November 12, 2020

Published in Issue

Year 2020 Volume: 5 Number: 2

APA
Aksoy Derya, Y., Çolak, C., Kayhan Tetik, B., Yılmaz, A. N., & Özşahin, Z. (2020). Estimation of Factors Related to Premenstrual Syndrome in Female Students by Using Artificial Neural Network Model. The Journal of Cognitive Systems, 5(2), 88-93. https://izlik.org/JA63AH53TT
AMA
1.Aksoy Derya Y, Çolak C, Kayhan Tetik B, Yılmaz AN, Özşahin Z. Estimation of Factors Related to Premenstrual Syndrome in Female Students by Using Artificial Neural Network Model. JCS. 2020;5(2):88-93. https://izlik.org/JA63AH53TT
Chicago
Aksoy Derya, Yeşim, Cemil Çolak, Burcu Kayhan Tetik, Ayşe Nur Yılmaz, and Zeliha Özşahin. 2020. “Estimation of Factors Related to Premenstrual Syndrome in Female Students by Using Artificial Neural Network Model”. The Journal of Cognitive Systems 5 (2): 88-93. https://izlik.org/JA63AH53TT.
EndNote
Aksoy Derya Y, Çolak C, Kayhan Tetik B, Yılmaz AN, Özşahin Z (December 1, 2020) Estimation of Factors Related to Premenstrual Syndrome in Female Students by Using Artificial Neural Network Model. The Journal of Cognitive Systems 5 2 88–93.
IEEE
[1]Y. Aksoy Derya, C. Çolak, B. Kayhan Tetik, A. N. Yılmaz, and Z. Özşahin, “Estimation of Factors Related to Premenstrual Syndrome in Female Students by Using Artificial Neural Network Model”, JCS, vol. 5, no. 2, pp. 88–93, Dec. 2020, [Online]. Available: https://izlik.org/JA63AH53TT
ISNAD
Aksoy Derya, Yeşim - Çolak, Cemil - Kayhan Tetik, Burcu - Yılmaz, Ayşe Nur - Özşahin, Zeliha. “Estimation of Factors Related to Premenstrual Syndrome in Female Students by Using Artificial Neural Network Model”. The Journal of Cognitive Systems 5/2 (December 1, 2020): 88-93. https://izlik.org/JA63AH53TT.
JAMA
1.Aksoy Derya Y, Çolak C, Kayhan Tetik B, Yılmaz AN, Özşahin Z. Estimation of Factors Related to Premenstrual Syndrome in Female Students by Using Artificial Neural Network Model. JCS. 2020;5:88–93.
MLA
Aksoy Derya, Yeşim, et al. “Estimation of Factors Related to Premenstrual Syndrome in Female Students by Using Artificial Neural Network Model”. The Journal of Cognitive Systems, vol. 5, no. 2, Dec. 2020, pp. 88-93, https://izlik.org/JA63AH53TT.
Vancouver
1.Yeşim Aksoy Derya, Cemil Çolak, Burcu Kayhan Tetik, Ayşe Nur Yılmaz, Zeliha Özşahin. Estimation of Factors Related to Premenstrual Syndrome in Female Students by Using Artificial Neural Network Model. JCS [Internet]. 2020 Dec. 1;5(2):88-93. Available from: https://izlik.org/JA63AH53TT