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Data mining approach for prediction umbilical cord wrapping around the fetus and investigating effective factors | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 1، دوره 54، شماره 2، اسفند 2022، صفحه 131-140 اصل مقاله (622.79 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2022.21360.5283 | ||
| نویسندگان | ||
| najmeh abedini* 1؛ fatemeh moayyedi2؛ sayed Ebrahim Dashti3 | ||
| 1Zand higher education, Shiraz, Iran | ||
| 2Department of Computer Engineering, Larestan Higher Education Complex, Lar, Iran | ||
| 3Department of Computer Engineering, Jahrom Branch, Islamic Azad University, Jahrom, Iran | ||
| چکیده | ||
| Today, in medical knowledge, data collection on various diseases is very important. One of the important issues in the medical world is the baby’s birth and its related issues. The relationship between mother and fetus is by the umbilical cord which is responsible for the development of the fetus. In this article, using data mining methods, the occurrence of umbilical cord torsion around the fetus is predicted, we also investigated some factors that can affect this event. Based on the studying articles on fetus birth and its factors, and consultation with gynecologists, the new and comprehensive questionnaire was designed on factors affecting the wrapping of the umbilical cord around the fetus, including 31 features that were completed by 140 samples of pregnant mothers. Then, the questionnaire was evaluated by Cronbach’s Alpha. Since the obtained dataset was imbalanced it was balanced with SMOTE technique. We compared different classification methods, including SVM, Random Forest, KNN, and Naïve Base for prediction, which KNN had the best result accuracy of 81%. Finally, to extract effective factors some association rule mining methods such as Predictive Apriori, and FP-growth were applied. the results show nutrition, blood pressure, diabetes, fetus number, and Internet usage can have more impact on wrapping the umbilical cord around the fetus. | ||
| کلیدواژه‌ها | ||
| Association rules mining؛ SMOTE؛ KNN؛ fetus؛ umbilical cord prediction | ||
| مراجع | ||
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[1] A. Linden, P.R. Yarnold, Using data mining techniques to characterize participation in observational studies, Journal of Evaluation in Clinical Practice, 22(6) (2016) 839-847.
[2] J.F. Clapp, B. Lopez, S. Simonean, Nuchal cord and neurodevelopmental performance at 1 year, The Journal of the Society for Gynecologic Investigation: JSGI, 6(5) (1999) 268-272.
[3] G. Hankins, R.R. Snyder, J.C. Hauth, L. Gilstrap 3rd, T. Hammond, Nuchal cords and neonatal outcome, Obstetrics and gynecology, 70(5) (1987) 687-691.
[4] D. Shere, A. Anyaegbuam, Prenatal ultrasonographic morphologic assessment of the umblical cord: a review, Obstet Gynecol Surv (USA), 52(8) (1997) 506-523.
[5] G.R. Gutiérrez, S.E. Razo, A.C. Curiel, A.P.P. de León, Color Doppler flowmetry values in fetuses with nuchal cord encirclement, Ginecologia y obstetricia de Mexico, 68 (2000) 401-407.
[6] A. Funk, W. Heyl, R. Rother, M. Winkler, W. Rath, Subpartal diagnosis of umbilical cord encirclement using color-coded Doppler ultrasonography and correlation with cardiotocographic changes during labor, Geburtshilfe und Frauenheilkunde, 55(11) (1995) 623-627.
[7] Tehran University Medical Journal, 63(12) (2005) 991-997.
[8] U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, Knowledge Discovery and Data Mining: Towards a Unifying Framework, in: KDD, 1996, pp. 82-88.
[9] S. Pereira, F. Portela, M.F. Santos, J. Machado, A. Abelha, Predicting type of delivery by identification of obstetric risk factors through data mining, Procedia Computer Science, 64 (2015) 601-609.
[10] N. Chamidah, I. Wasito, Fetal state classification from cardiotocography based on feature extraction using hybrid K-Means and support vector machine, in: 2015 international conference on advanced computer science and information systems (ICACSIS), IEEE, 2015, pp. 37-41.
[11] H.-Y. Chen, C.-H. Chuang, Y.-J. Yang, T.-P. Wu, Exploring the risk factors of preterm birth using data mining, Expert systems with applications, 38(5) (2011) 5384-5387.
[12] R.W. Bendon, S.P. Brown, M.G. Ross, In vitro umbilical cord wrapping and torsion: possible cause of umbilical blood flow occlusion, The Journal of Maternal-Fetal & Neonatal Medicine, 27(14) (2014) 1462-1464.
[13] t. ashraf, Umbilical cord entanglement and intrapartum complications, Journal of Shahrekord Uuniversity of Medical Sciences, 6(2) (2004) 44-49.
[14] L. Mallick, J. Yourkavitch, C. Allen, Trends, determinants, and newborn mortality related to thermal care and umbilical cord care practices in South Asia, BMC pediatrics, 19(1) (2019) 1-16.
[15] A. Krzyżanowski, M. Kwiatek, T. Gęca, A. Stupak, A. Kwaśniewska, Modern ultrasonography of the umbilical cord: prenatal diagnosis of umbilical cord abnormalities and assessement of fetal wellbeing, Medical science monitor: international medical journal of experimental and clinical research, 25 (2019) 3170.
[16] G.A. Pradipta, R. Wardoyo, A. Musdholifah, I.N.H. Sanjaya, Improving classifiaction performance of fetal umbilical cord using combination of SMOTE method and multiclassifier voting in imbalanced data and small dataset, Int. J. Intell. Eng. Syst., 13(5) (2020) 441-454.
[17] M. Shuja, S. Mittal, M. Zaman, Effective prediction of type ii diabetes mellitus using data mining classifiers and SMOTE, in: Advances in computing and intelligent systems, Springer, 2020, pp. 195-211.
[18] R. Heale, A. Twycross, Validity and reliability in quantitative studies, Evidence-based nursing, 18(3) (2015) 66-67.
[19] A. Ramezankhani, O. Pournik, J. Shahrabi, F. Azizi, F. Hadaegh, D. Khalili, The impact of oversampling with SMOTE on the performance of 3 classifiers in prediction of type 2 diabetes, Medical decision making, 36(1) (2016) 137-144.
[20] W. Xing, Y. Bei, Medical health big data classification based on KNN classification algorithm, IEEE Access, 8 (2019) 28808-28819.
[21] S. Vijayarani, S. Dhayanand, Data mining classification algorithms for kidney disease prediction, Int J Cybernetics Inform, 4(4) (2015) 13-25.
[22] P. Edastama, A.S. Bist, A. Prambudi, Implementation Of Data Mining On Glasses Sales Using The Apriori Algorithm, International Journal of Cyber and IT Service Management, 1(2) (2021) 159-172.
[23] C. Wang, X. Zheng, Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint, Evolutionary Intelligence, 13(1) (2020) 39-49.
[24] B.M. Patil, R.C. Joshi, D. Toshniwal, Classification of type-2 diabetic patients by using Apriori and predictive Apriori, International Journal of Computational Vision and Robotics, 2(3) (2011) 254-265.
[25] I.H. Sarker, Machine Learning: Algorithms, Real-World Applications and Research Directions, SN Computer Science, 2(3) (2021) 160.
[26] T. Scheffer, Finding association rules that trade support optimally against confidence, Intelligent Data Analysis, 9(4) (2005) 381-395.
[27] M. Kavitha, S. Selvi, Comparative study on Apriori algorithm and Fp growth algorithm with pros and cons, International Journal of Computer Science Trends and Technology (I JCS T)–Volume, 4 (2016).
[28] J. Xu, Y. Zhang, D. Miao, Three-way confusion matrix for classification: A measure driven view, Information sciences, 507 (2020) 772-794.
[29] S. Stiernborg, S. Ervik, Evaluation of Machine Learning Classification Methods: Support Vector Machines, Nearest Neighbour and Decision Tree, in, 2017. | ||
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آمار تعداد مشاهده مقاله: 414 تعداد دریافت فایل اصل مقاله: 400 |
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