Proceedings of International Conference on Applied Innovation in IT
2023/11/30, Volume 11, Issue 2, pp.101-105

Prediction of Child Birth Delivery Mode Using Hybrid-Boosting Ensemble Machine Learning Model


Mamatha Sekireddy and Sudha Thatimakula


Abstract: Maternal health is a critical aspect of public health that affects the well-being of both mother and infant. Despite the medical breakthroughs, maternal mortality rates remain high, particularly in developing countries. AI- based models provide new ways to analyze and interpret medical data, which can ultimately improve maternal and fetal health outcomes. The present study proposes a hybrid model for the maternal mode of delivery classification in pregnancy, which utilizes the strength of Ada Boost, Gradient Boost, and XG Boost algorithms. The proposed model culminates the three algorithms to improve the accuracy and efficiency of childbirth delivery classification method in pregnant women. The dataset used in this study consists of features such as age, systolic and diastolic blood pressure, blood sugar, body temperature, heart rate, placenta previa, and gravida. The dataset is divided into training and testing sets, where 70% of the data is used for training and the rest 30% for testing. The output of the Ada Boost, Gradient Boost, and XG Boost classifier is considered, and a maximum probability voting system selects the output with the highest probability as the most correct one. Performance is evaluated using various metrics, such as accuracy, precision, recall, and F1 score as an outcome where the XG Boost showed an accuracy of 97.07%, the Ada Boost showed 85.02%, and the Gradient Boost showed 92.51% respectively. Results also conspicuously showed that the proposed model achieves the highest accuracy of t98.05%, with 97.9% precision, 97.9% recall, and an F1 score of 97.9% on the testing dataset. The hybrid model proposed in this study has the potentiality to improve the accuracy and efficiency of the maternal mode of delivery classification in pregnancy, leading to better health outcomes for pregnant women and their babies.

Keywords: Ensemble Methods, Machine Learning, Artificial Intelligence, Voting.

DOI: 10.25673/112999

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References:

  1. M. Najrul Islam, "Exploring Machine learning algorithms to find the best features for predicting mode of child birth," Digital Object Identifier 10.1109/ACCESS.2020.3045469.
  2. Md. Kowsher, "Predicting the Appropriate Mode of Childbirth using Machine Learning Algorithms," IJACSA, vol. 12, no. 5, 2021.
  3. S. S. Hussain, "A Comparative Study of Supervised Machine Learning Techniques for Diagnosing Mode of Delivery in Medical Sciences," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 10, no. 12, 2019.
  4. S. A. Abbas, "Cause Analysis of Caesarean Sections and Applications of Machine Learning Methods for Classification of Birth Data," Digital Object Identifier 10.1109/ACCESS. 2018.2879115, IEEE Access, 2018.
  5. G. A. Suciningsih, "Comparative analysis of Naïve Bayes and Decision Tree C4.5 for Cesarean Section Prediction," Journal of Soft Computing Exploration, ISSN: 2746-0991.
  6. A. Crossman, "Correlation Analysis in Research," from www.thoughtco.com: https://www.thoughtco.com/what-is-correlation-analysis-3026696.
  7. Rocca, "Ensemble-methods-bagging-boosting-and-stacking," from towardsdatascience.com: https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205.
  8. K. Dembczynski, W. Kotłowski, and S. Słowi ´ nski, "Maximum Likelihood Rule Ensembles," in Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland: Association for Computing Machinery, p. 224–31. doi: 10.1145/1390156.1390185.
  9. F. Leon, S. A. Floria, and C. Ba˘dica˘, "Evaluating the Effect of Voting Methods on Ensemble-based Classification," in 2017 IEEE International Conference on Innovations in Intelligent Systems and Applications (INISTA), Gdynia: Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/INISTA.2017.8001122.
  10. D. M. Powers, "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation," arXiv.

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DOI: http://dx.doi.org/10.25673/115729


        

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