ArticlePloS one2022
Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 97 citations in OpenAlex.
- Maternal early warning scores shown to be methodologically weak and at high risk of bias.Journal of clinical epidemiology · 2025Pooled it
- High-risk pregnancy prediction using Taguchi-optimized machine learning methods and TOPSIS-based model selection.Scientific reports · 2026Article
- The impact of social determinants of health on infant and maternal health using a reproductive justice lens.BMC pregnancy and childbirth · 2025Article
- Application of Proteomics in Maternal and Neonatal Health: Advancements and Future Directions.Proteomics. Clinical applications · 2025Review
- Maternal Health Risk Detection: Advancing Midwifery with Artificial Intelligence.Healthcare (Basel, Switzerland) · 2025Article
- AI's ongoing impact: Implications of AI's effects on health equity for women's healthcare providers.Revista panamericana de salud publica = Pan American journal of public health · 2025Article
- Predicting maternal risk level using machine learning models.BMC pregnancy and childbirth · 2024Article
- Leveraging machine learning models for anemia severity detection among pregnant women following ANC: Ethiopian context.BMC public health · 2024Article
- Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.BMC medical informatics and decision making · 2024Article
- Optimized virtual reality design through user immersion level detection with novel feature fusion and explainable artificial intelligence.PeerJ. Computer science · 2024Article
- CVG-Net: novel transfer learning based deep features for diagnosis of brain tumors using MRI scans.PeerJ. Computer science · 2024Article
- Improving prediction of maternal health risks using PCA features and TreeNet model.PeerJ. Computer science · 2024Article
- Estimation of Obesity Levels through the Proposed Predictive Approach Based on Physical Activity and Nutritional Habits.Diagnostics (Basel, Switzerland) · 2023Article
- An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features.Diagnostics (Basel, Switzerland) · 2023Article
- Deep hybrid model for maternal health risk classification in pregnancy: synergy of ANN and random forest.Frontiers in artificial intelligence · 2023Article
- Article
- A CNN-GRU framework for stroke-heart attack prediction using IMOWPA-tuned SMOTE and LZMA compression.Digital healthArticle
- Artificial intelligence in maternal and child health: Current applications, translational gaps, and future research priorities.Women's health (London, England)Review
- Development of an advanced maternal health risk detection system using enhanced XGBOOST and blending models.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.