Determination and Classification of Importance of Attributes Used in Diagnosing Pregnant Women's Birth Method

The rapid development of information technologies enables successful results in computer-aided studies. This has led researchers to investigate the usability of technologies such as computer and software supported systems, machine learning, and artificial intelligence in many studies. One of these areas is health. For example, in order not to risk the condition of the mother and baby, in some cases, it is very important to correctly determine the times when the cesarean operation, which is mandatory, is mandatory. In this context, in order to make a faster and more accurate decision, it is very important to determine which attributes and how important the level is in making obligatory cesarean. In this study, to determine whether or not caesarean is necessary in the literature, the importance level of the five criteria taken into consideration has been determined and an attribute determination has been carried out and then a classification has been made. The data set used belongs to 80 pregnant women with 6 attributes. Although the same data set was previously classified with different methods, no study was foundon determining the significance levels of the attributes and using artificial neural networks as a method. For this reason, in this study, the feature was determined using an adaptive nerve-fuzzy classifier and classified using artificial neural networks.When the results are examined, it is concluded that the importance levels of the attributes are different. Although the values such as accuracy, Sensitivity, and Specificity calculated to evaluate the classification results were found to be quite high forthe training set, it was observed that the desired success was not achieved in the test data. While this result is promising, it also reveals the need to increase the learning performed with larger data sets.

PDF

___

Alan, M. A. (2012). Data mining and an application on graduate students’ data. Dumlupınar University Journal of Social Sciences, (33), 165-174.
Alan, M. (2004). Sivas Erzincan Kalkınma Projesi (SEKP) verilerinin veri madenciliği ile sınıflandırılması ve kümelenmesi. Manas Journal of Social Studies, 3(2), 129-144.
Alptekin, N., Yeşilaydın, G., (2015). Classifying OECD countries according to health ındicators using fuzzy clustering analysis. Journal of Business Reasearch, 7(4), 137-155.
Al-Tashi, Q., Kadir, S. J. A., Rais, H. M., Mirjalili, S., Alhussian, H. (2019). Binary optimization using hybrid grey wolf optimization for feature selection. IEEE Access, 7, 39496-39508.
Amin, M. Z., Ali, A. (2018). Performance evaluation of supervised machine learning classifiers for predicting healthcare operational decisions. Wavy AI Research Foundation: Lahore.
Bilimleri, M. (2019). Discriminative features for energy-constraıned devices on transportation mode detection. Journal of Engineering Sciences and Design, 7(1), 90-102.
Budak, H., Erpolat, S. (2012). Comparison of artificial neural networks and logistic regression analysis in the credit risk prediction . AJIT‐e: Online Academic Journal of Information Technology, 3(9), 23-30.
Bulut, F. (2016). Right career choice with multilayer perceptron, Anadolu University Journal of Science and Technology A- Applied Sciences and Engineering. 17(1), 97-109.
Çelik, S., Bozkurt, Ö. Ç, Çeşmeli M. Ş. (2018). Selectıon of attrıbutes and classificatıon of vertebral column data set with neuro-fuzzy classifıer. Journal of Management Information Systems, 4 (1), 39-52.
Çeşmeli, M. Ş., Bozkurt, Ö. Ç., Kalkan, A., Pençe, İ. (2015). Yönetim bilişim sistemleri bölümü öğrencilerinin yönetim ve bilişim derslerindeki başarılarının veri madenciliği yöntemleri ile incelenmesi. Journal of Management Information Systems, 1(2), 36-47.
Çetişli, B. ( 2006). Using neuro-fuzzy classifier with lingustic hedges for feature selection . Journal of Engineering and Architecture Faculty of Eskişehir Osmangazi Universitiy, 19 (2), 109-130.
Çetişli, B. (2009). Gene selection by using a linguistic hedged adaptive neuro-fuzzy classifier for cancer classification. Signal Processing and Communications Applications Conference, (17), 257-260.
Çolak, C., Çolak, M. C., Atıcı, M. A. (2005). An artificial neural network for the prediction of atherosclerosis. Journal of Ankara University School of Medicine, 58(04), 159-162.
Dreiseitl, S., Ohno-Machado, L. (2002). Logistic regression and artificial neural network classification models: a methodology review. Journal of Biomedical İnformatics, 35(5-6), 352-359.
El-Bouri, A., Balakrishnan, S., Popplewell, N. (2000). Sequencing jobs on a single machine: a neural network approach. European Journal of Operational Research, 126, 474–490.
El_Jerjawi, N. S., Abu-Naser, S. S. (2018). Diabetes prediction using artificial neural network. International Journal of Advanced Science and Technology, 121, 55-64.
Emhan, Ö., Akın, M. (2019). Filtreleme tabanlı öznitelik seçme yöntemlerinin anomali tabanlı ağ saldırısı tespit sistemlerine etkisi. DÜMF Mühendislik Dergisi, 10(2), 549-559.
Fırat, M., Yurdusev, M. A., Mermer, M. (2008). Monthly water demand forecastıng by adaptive neurofuzzy inference system approach. Journal of the Faculty of Engineering and Architecture of Gazi University, 23(2), 449-457.
Gharehchopogh, F. S., Mohammadi, P., Hakimi, P. (2012). Application of decision tree algorithm for data mining in healthcare operations: a case study. International Journal of Computer Applications, 52(6), 21-26.
Gündüz, A. E., Temizel, A., Temizel, T. T. (2013). Feature detection and tracking for extraction of crowd Dynamics. Signal Processing and Communications Applications Conference, (21), 1-4.
Haltaş, A., Alkan, A. (2016). Automatic classification of the medical documents on the medline database into relevant cancer types. International Journal of Informatics Technologies, 9(2), 181-186.
Hill, T., Marquez, L., O'Connor, M., Remus, W. (1994). Artificial neural network models for forecasting and decision making. International Journal of Forecasting, 10(1), 5-15.
Hoskins, J. C., Himmelblau, D. M. (1988). Artificial neural network models of knowledge representation in chemical engineering. Computers & Chemical Engineering, 12(9-10), 881- 890.
Ion, R. M., Munteanu, D., Cocina, G. C. (2009). Concept of artificial neural network (ANN) and its application in cerebral aneurism with multi walls carbon nanotubes (MWCNT). In Proceedings of the 10th WSEAS international conference on Neural networks.
Karahan, M. (2015). A case study on forecasting of tourism demand with artificial neutral network method. Suleyman Demirel University The Journal of Faculty of Economics and Administrative Sciences, 20(2), 195-209.
Kaya, C., Erkaymaz, O., Ayar, O., Özer, M. (2017). Classification of diabetic retinopathy disease from Video-Oculography (VOG) signals with feature selection based on C4. 5 decision tree. Medical Technologies National Congress, 1-4.
Kaya, Y., Ertuğrul, Ö. F. (2016). A novel feature extraction approach for text-based language identification: Binary patterns. Journal of the Faculty of Engineering and Architecture of Gazi University, 31(4), 1085-1094.
Kayım, G., Sarı, C., Akgül, C. B., (2013). Facial feature selection for gender recognition based on random decision forests. Signal Processing and Communications Applications Conference, (21), 1-4.
Kaynar, O., Taştan, S., Demirkoparan, F. (2011). Yapay sinir ağlari ile doğalgaz tüketim tahmini. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 25, 463-47.
Kaynar, O. ve Taştan, S. (2009). Zaman serisi analizinde mlp yapay sinir ağları ve arıma modelinin karşılaştırılması. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (33), 161-172.
Khan, J., Wei, J. S., Ringner, M., Saal, L. H., Ladanyi, M., Westermann, F., ..., Meltzer, P. S. (2001).
Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine, 7(6), 673–679.
Machine Learning Repository, https://archive.ics.uci.edu/ml/datasets/Caesarian+Section+Classification+Dataset, (05.04.2019).
Mafarja, M., Aljarah, I., Faris, H., Hammouri, A. I., Ala’M, A. Z., Mirjalili, S. (2019). “Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Systems with Applications, 117, 267-286.
Nagy, H. M., Watanabe, K., Hirano, M. (2002) Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulic Engineering, 128(6), 588-595.
Park, D. C., El-Sharkawi, M. A., Marks, R. J., Atlas, L. E., Damborg, M. J. (1991). Electric load forecasting using an artificial neural network. IEEE Transactions on Power Systems, 6(2), 442-449.
Partal, T., Kahya, E., Cığızoğlu, K. (2011). Estimation of precipitation data using artificial neural networks and wavelet transform. İTÜDERGİSİ/d, 7(3), 73-85.
Pençe, İ., Çetişli, B. (2013). Handwriting character modeling with implicit curves and classification. Sigma, 5, 1-7.
Polat, H., Özerdem, M. S. (2016), Classification of emotions based on audio-visual stimulus by eeg signals . DÜMF Mühendislik Dergisi, 7(1), 33-40.
Selim, S., Demirbilek, A. (2009). Türkiye’deki konutlarin kira değerinin analizi: hedonik model ve yapay sinir ağlari yaklaşımı. Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi , 1(1), 73-90.
Türkoğlu, İ., Arslan, A. (1996). Yapay sinir ağları ile bozuk örüntü tanıma. Fırat Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 8(1), 147-158.
Ulusoy, T. (2010). Artificial neural network modelling to predict the ise index using feed forward network architecture. International Journal of Economic & Administrative Studies, 2(5), 21- 40.
Yakut, E., Gemici, E. (2017). Predicting stock return classification through LR, C5.0, CART and SVM methods, and comparing the methods used: an application at BIST in Turkey. Ege Academic Review, 17(4), 461-479.
Yoldaş, M., Şakar, M. O., Dirlikli, M., Kılınç, O. E. S. (2014). Mamografi imgelerinden HOG öznitelikleri çıkartılarak hastaların kanser seviyelerinin belirlenmesi. TMMOB EMO Ankara Şubesi Haber Bülteni İlk Bildiriler Konferansı, 14-16.
Zhang, Y., Ding, X., Liu, Y., Griffin, P. J. (1996). An artificial neural network approach to transformer fault diagnosis. IEEE Transactions on Power Delivery, 11(4), 1836-1841.