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

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  • Sümeyye Çelik

Abstract

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 found on 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 for the 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.

Suggested Citation

  • Sümeyye Çelik, 2020. "Determination and Classification of Importance of Attributes Used in Diagnosing Pregnant Women's Birth Method," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(2), pages 261-274, December.
    • Handle: RePEc:anm:alpnmr:v:8:y:2020:i:2:p:261-274
    DOI: http://dx.doi.org/10.17093/alphanumeric.757769
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    References listed on IDEAS

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    1. Hill, Tim & Marquez, Leorey & O'Connor, Marcus & Remus, William, 1994. "Artificial neural network models for forecasting and decision making," International Journal of Forecasting, Elsevier, vol. 10(1), pages 5-15, June.
    2. El-Bouri, Ahmed & Balakrishnan, Subramaniam & Popplewell, Neil, 2000. "Sequencing jobs on a single machine: A neural network approach," European Journal of Operational Research, Elsevier, vol. 126(3), pages 474-490, November.
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    Cited by:

    1. Sümeyye Çelik & Melike Şişeci Çeşmeli & İhsan Pençe & Özlem Çetinkaya Bozkurt, 2022. "Classification of Autism Spectrum Disorder for Adolescents Using Artificial Neural Networks," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 10(1), pages 15-24, June.

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    More about this item

    Keywords

    Adaptive Neuro-Fuzzy Classifier; Artificial Neural Networks; Attribute Selection; Caesarean; Classification;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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