Improving Maternal Risk Analysis in Public Health Systems

Pereira, Silas S. L.; Filho, Raimundo Valter Costa; Ramos, Ronaldo; Oliveira, Mauro; Moreira, Mário W. L.; Rodrigues, Joel J. P. C.; Šolić, Petar

doi:10.23919/splitech49282.2020.9243769

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…The ensemble classifier achieved an 86% of accuracy score for birth mode classification. The maternal health risk prediction based on electronic health registries using an RF is proposed in [31]. The relevant features are selected by the recursive feature elimination (RFE) for the decision-making task.…”

Section: Related Workmentioning

confidence: 99%

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Raza

Siddiqui²,

Munir³

et al. 2022

PLoS ONE

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Maternal health is an important aspect of women’s health during pregnancy, childbirth, and the postpartum period. Specifically, during pregnancy, different health factors like age, blood disorders, heart rate, etc. can lead to pregnancy complications. Detecting such health factors can alleviate the risk of pregnancy-related complications. This study aims to develop an artificial neural network-based system for predicting maternal health risks using health data records. A novel deep neural network architecture, DT-BiLTCN is proposed that uses decision trees, a bidirectional long short-term memory network, and a temporal convolutional network. Experiments involve using a dataset of 1218 samples collected from maternal health care, hospitals, and community clinics using the IoT-based risk monitoring system. Class imbalance is resolved using the synthetic minority oversampling technique. DT-BiLTCN provides a feature set to obtain high accuracy results which in this case are provided by the support vector machine with a 98% accuracy. Maternal health exploratory data analysis reveals that the health conditions which are the strongest indications of health risk during pregnancy are diastolic and systolic blood pressure, heart rate, and age of pregnant women. Using the proposed model, timely prediction of health risks associated with pregnant women can be made thus mitigating the risk of health complications which helps to save lives.

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Section: Related Workmentioning

confidence: 99%

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Raza

Siddiqui²,

Munir³

et al. 2022

PLoS ONE

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“…Assaduzzaman et al (2023) focused on ML model to develop risk factors for maternal health using a dataset that preprocessed and applied feature engineering techniques to develop a prediction model using RF and other ML classifiers; among them, RF achieved an accuracy of 90% which was a most top model. Pereira et al (2020) addressed the health monitoring system of maternal risk factors using six ML models and applied the feature elimination technique RFE to the feature set. The RF classifier with RFE achieved the highest mean accuracy of 93.24%.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-based maternal health risk prediction model for IoMT framework

Mondal,

Nag,

Barman

et al. 2023

IJERR

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The Internet of Things (IoT) is vital as it offers extensive applicability in various fields, including healthcare. In the context of the risk level during pregnancy, to monitor and predict abnormalities, IoT devices provide a means to collect real-time health data, enabling continuous monitoring and analysis in the Internet of Medical Things (IoMT) environments. By integrating IoT devices into the system, crucial signs such as Heart Rate (HR), Systolic and Diastolic Blood Pressure (BP), Fetal Movements (FM), and Temperature (T) can be tracked remotely and non-invasively. This allows for the timely detection of abnormalities or potential risk factors during pregnancy, empowering healthcare professionals to intervene proactively and provide personalized care. This research focuses on developing a system for observing and predicting the maternal risk level in the IoT environment, mainly in remote areas. The goal is to improve maternal health and reduce maternal and child mortality rates, a significant decline according to United Nations targets for 2030. The research utilizes analytical tools and Machine Learning (ML) algorithms to analyze health data and risk factors associated with pregnancy. The acquired dataset contains various risk factors categorized and classified based on intensity. After comparing different ML models’ experimental results, Exploratory Data Analysis (EDA) approaches to determine the most effective risk factors. The fine-tuned Random Forest Classifier (RF) achieves the highest accuracy of 93.14%. An Android-based application has also been developed to deploy the prediction model to determine risk levels based on the different parameters.

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Analysis of maternal health risk using computational models

Ravi,

Joshi

et al. 2022

2022 International Conference on Smart and Sustainable Technologies in Energy and Power Sectors (SSTEPS)

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Improving Maternal Risk Analysis in Public Health Systems

Cited by 3 publications

References 15 publications

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

Machine Learning-based maternal health risk prediction model for IoMT framework

Analysis of maternal health risk using computational models

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