Neonatal Mortality Prediction in NICUs: A Machine Learning Approach

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Shariat Nastaran; Kargari Mehrdad; Alavi Meysam; Shariat Shaghayegh; Valiollahi Arefeh

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2024 | Issue:10

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Title

Neonatal Mortality Prediction in NICUs: A Machine Learning Approach

Author(s)

Shariat Nastaran | Kargari Mehrdad | Alavi Meysam | Shariat Shaghayegh | Valiollahi Arefeh

Keywords

Neonatal Mortality

Machine Learning

Classification

Convolutional Neural Network

Abstract

Infants during the neonatal period, defined as the first four weeks after birth, exhibit the highest probability of mortality. This vulnerable stage of early life, characterized by heightened rates of mortality and neonatal diseases, underscores the susceptibility of neonatal life during this period. Consequently, delineating the mortality profile of neonates within the community is a pivotal strategy for identifying causal factors and presenting findings, thus constituting one of the most crucial approaches for enhancing neonatal health outcomes. In the field of medical science, one of the most prominent applications of Machine Learning is in disease diagnosis and prediction. Therefore, the aim of this research is to introduce two methodologies designed to forecast the likelihood of Neonatal Mortality. The first approach relies on utilizing Machine Learning Classification algorithms, while the second approach employs Convolutional Neural Networks (CNNs) on non-image data. To assess the developed models, metrics including Accuracy, Recall, Precision, and F-Score have been utilized. Among the methods used, the second approach, which uses CNN, performs better in predicting the probability of infant mortality during the neonatal period with 98% accuracy.

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APA: Copy

Shariat, Nastaran, Kargari, Mehrdad, Alavi, Meysam, Shariat, Shaghayegh, & Valiollahi, Arefeh. (2024). Neonatal Mortality Prediction in NICUs: A Machine Learning Approach. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/1147389/en

Vancouver: Copy

Shariat Nastaran, Kargari Mehrdad, Alavi Meysam, Shariat Shaghayegh, Valiollahi Arefeh. Neonatal Mortality Prediction in NICUs: A Machine Learning Approach. 2024. Available from: https://sid.ir/paper/1147389/en

IEEE: Copy

Nastaran Shariat, Mehrdad Kargari, Meysam Alavi, Shaghayegh Shariat, and Arefeh Valiollahi, “Neonatal Mortality Prediction in NICUs: A Machine Learning Approach,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2024, [Online]. Available: https://sid.ir/paper/1147389/en

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