Fetal Health Prediction using neural networks

Chowdhury, Ayanesh; Chahar, Ankit; Rohan, Eswara,; Raheem, Mohammed Abdul; Ehetesham, Shaik; Thulasidoss, Bharath Kumar

doi:10.1109/icaccs54159.2022.9784987

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…They have indicated that maternal health is the main reason behind infant mortality. Furthermore, in the study, Chowdhury et al [8] aim to predict the health of the fetus or unborn child using a neural network. They claim that out of 100 fetal births, 9 have some sort of health issue.…”

Section: Introductionmentioning

confidence: 99%

A deep learning and machine learning approach to predict neonatal death in the context of São Paulo

Raihan,

Saha,

Gupta

et al. 2024

IJPHS

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Neonatal death is still a concerning reality for underdeveloped and even for some of the developed countries. Worldwide data indicate that 26.693 babies out of 1,000 births according to Macro Trades. To reduce the death early prediction of endangered baby is crucial. An early prediction enables the opportunity to take ample care of the child and mother so that an early child death can be avoided. Machine learning was used to figure out whether a newborn baby is at risk. To train the predictive model historical data of 1.4 million newborn child data was used. Machine learning and deep learning techniques such as Logical regression, K nearest neighbor, Random Forest classifier, Extreme gradient boosting (XGboost), convolutional neural network, long short-term memory (LSTM). were implemented using the dataset to find out the most robust model which model is the most accurate to identify the mortality of a newborn. From all the machine learning algorithms, the XGboost and random classifier had the best accuracy with 94%, and from the deep learning model, the LSTM had the best outcome with 99% accuracy. Thus, using LSTM of the model shall be best suited to predict whether precaution for a child is necessary.

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Section: Introductionmentioning

confidence: 99%