Prediction of fetal weight based on back propagation neural network optimized by genetic algorithm

Gao, Hong; Wu, Cuiyun; Huang, D.; Zha, Dahui; Zhou, Cuiping

doi:10.3934/mbe.2021222

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…In future work, sensors that can reflect the wrist and hand function state (such as bending sensor) will be used to collect wrist and hand movement data, and a model will be established to predict the score of Fugl-Meyer wrist and hand part, so as to more comprehensively and carefully reflect the whole upper limb movement function of the patient. In the future, other deep learning models will be used [20,21].…”

Section: Resultsmentioning

confidence: 99%

Design of Intelligent Rehabilitation Evaluation Scale for Stroke Patients Based on Genetic Algorithm and Extreme Learning Machine

Zhi

Meng

2022

Journal of Sensors

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The rehabilitation of stroke patients is a long-term process. To realize the automation and quantification of upper limb rehabilitation assessment of stroke patients, an automatic prediction model of rehabilitation evaluation scale was established by extreme learning machine (ELM) according to Fugl-Meyer motor function assessment (FMA). Four movements in the shoulder and elbow joints of FMA were selected. Two acceleration sensors fixed on the forearm and upper arm of the hemiplegic side were used to collect the motion data of 35 patients. After preprocessing and feature extraction, the feature selection was carried out based on genetic algorithm and ELM, and the single-action model and comprehensive prediction model were established, respectively. The results show that the model can accurately and automatically predict the shoulder and elbow score of FMA, and the root mean square error of prediction is 2.16. This method breaks through the limitations of subjectivity, time-consuming and dependence on rehabilitation doctors in the traditional evaluation. It can be easily used in the assessment of long-term rehabilitation.

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Section: Resultsmentioning

confidence: 99%

Design of Intelligent Rehabilitation Evaluation Scale for Stroke Patients Based on Genetic Algorithm and Extreme Learning Machine

Zhi

Meng

2022

Journal of Sensors

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“…Analysis based on the GA-BP neural networks for predicting the fetal weight is explained by Zhu, et al ( 2018) [31]. The main principle in predicting the parturient symphosio to fundal height, girth of abdominal measurement, abdominal palpitation as well as obstetric maternal ultrasound in a clinical practice.…”

Section: S Nomentioning

confidence: 99%

A Systematic Review on the Applications of Machine Learning for Fetal Birth Weight Prediction

2024

Int J Med Net

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In order to protect the maternal and infant safety, birth weight is an important indicator during fetal development. A doctor's experience in clinical practice, however, helps estimate birth weight by using empirical formulas based on the experience of the doctors. Recently, birth weights have been predicted using machine learning (ML) technologies. A machine learning model is built on the basis of a collection of attributes learns to predict predefined characteristics or results. Using a machine learning model, input and output are modeled together and then a set of models are trained on the data. It is possible to use machine learning for a variety of tasks such as predicting risks, diagnosing diseases, and classifying objects due to its scalability and flexibility, which are advantages over conventional methods. This research reviews the machine learning classification models used previously by various researchers to predict fetal weight. In this paper 85 studies were reviewed. Machine learning approach was considered as a better option to predict the fetal weight in all the studies included in this paper. The findings of this research show that the accuracy rate of using machine learning applications for fetal birth weight prediction is above 60% in all the studies reviewed.

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“…According to a comparison between the machine learning models, the hybrid LSTM model obtained the mean relative error (MRE) and the best accuracy of 0.933. Using the same techniques, in [18], the authors used the data for 80 women collected from a Chinese hospital. Therefore, this study aims to create a prediction model for fetal weight using the genetic algorithm to improve the Back Propagation Neural Network (GA-BPNN) to deal with the continuous change in the signs of pregnant women.…”

Section: Related Workmentioning

confidence: 99%

Birthweight Range Prediction and Classification: A Machine Learning-Based Sustainable Approach

Alabbad,

Ajibi,

Alotaibi

et al. 2024

MAKE

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An accurate prediction of fetal birth weight is crucial in ensuring safe delivery without health complications for the mother and baby. The uncertainty surrounding the fetus’s birth situation, including its weight range, can lead to significant risks for both mother and baby. As there is a standard birth weight range, if the fetus exceeds or falls below this range, it can result in considerable health problems. Although ultrasound imaging is commonly used to predict fetal weight, it does not always provide accurate readings, which may lead to unnecessary decisions such as early delivery and cesarian section. Besides that, no supporting system is available to predict the weight range in Saudi Arabia. Therefore, leveraging the available technologies to build a system that can serve as a second opinion for doctors and health professionals is essential. Machine learning (ML) offers significant advantages to numerous fields and can address various issues. As such, this study aims to utilize ML techniques to build a predictive model to predict the birthweight range of infants into low, normal, or high. For this purpose, two datasets were used: one from King Fahd University Hospital (KFHU), Saudi Arabia, and another publicly available dataset from the Institute of Electrical and Electronics Engineers (IEEE) data port. KFUH’s best result was obtained with the Extra Trees model, achieving an accuracy, precision, recall, and F1-score of 98%, with a specificity of 99%. On the other hand, using the Random Forest model, the IEEE dataset attained an accuracy, precision, recall, and F1-score of 96%, respectively, with a specificity of 98%. These results suggest that the proposed ML system can provide reliable predictions, which could be of significant value for doctors and health professionals in Saudi Arabia.

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Prediction of fetal weight based on back propagation neural network optimized by genetic algorithm

Cited by 9 publications

References 22 publications

Design of Intelligent Rehabilitation Evaluation Scale for Stroke Patients Based on Genetic Algorithm and Extreme Learning Machine

Design of Intelligent Rehabilitation Evaluation Scale for Stroke Patients Based on Genetic Algorithm and Extreme Learning Machine

A Systematic Review on the Applications of Machine Learning for Fetal Birth Weight Prediction

Birthweight Range Prediction and Classification: A Machine Learning-Based Sustainable Approach

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