Abstract
Perinatal asphyxia is one of the top three causes of neonatal death in developing countries, killing over 1.2 million newborns yearly. Asphyxia cannot be definitively diagnosed early on visually or physically; instead, it can only be diagnosed medically. In this research, an ensemble machine learning-based approach is proposed to detect infant asphyxia at the early stage. Mel-Frequency Cepstral Coefficients (MFCCs), which divide each feature’s values into the time domain and the frequency domain, were originally used in the technique to evaluate feature extraction methodologies. Unwanted noise, outliers, missing numbers, label encoding, and other difficulties are eliminated using pre-processing techniques. By applying the random oversampling (ROS) method, data balance is achieved. After analyzing and evaluating the performance of the proposed model, it is observed that the highest accuracy 99.29% is obtained using the combination of logistic regression and K-nearest neighbor with a 0.007% rate of error.
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Acknowledgements
The Baby Chillanto Data Base is a property of the Instituto Nacional de Astrofisica Optica y Electronica—CONACTY, Mexico. We like to thank Dr. Carlos A. Reyes-Garcia, Dr. Emilio Arch-Tirado and his INR-Mexico group, and Dr. Edger M. Garcia-Tamayo for their dedication of the collection of the infant cry data base. The authors would also like to graciously acknowledge the Information and Communication Technology (ICT) division of the Ministry of Posts, Telecommunications, and Information Technology of the Government of Bangladesh for supporting this research work through Grant No: 1280101-120008431-3631108.
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Uddin, K.M.M. et al. (2024). Toward Early Detection of Neonatal Birth Asphyxia Utilizing Ensemble Machine Learning Approach. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. IJCACI 2022. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-97-0180-3_4
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