ArticleJournal of healthcare engineering2021
A Machine Learning-Based Prediction Model for Preterm Birth in Rural India.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Machine learning to predict adverse perinatal outcomes: a systematic review and meta-analysis.EClinicalMedicine · 2026Article
- Prediction of Spontaneous Preterm Birth in the Second Trimester: Current Models, Limitations, and the Way Forward.Indian journal of pediatrics · 2026Review
- Analysis of electrohysterogram signals for predicting obstetric outcome using machine learning methods: a scoping review.BMC pregnancy and childbirth · 2026Article
- Artificial Intelligence and Machine Learning for predicting Major Obstetric Emergencies: Current Evidence, Clinical Translation, and Future Directions.International journal of women's health · 2026Review
- Early prediction of very and extreme preterm births using a one-class classification framework on electronic health records in UAE.Scientific reports · 2025Article
- Prediction of preterm birth from cervical length measurements in twin pregnancies using machine learning.Scientific reports · 2025Article
- Prediction of adverse pregnancy outcomes using machine learning techniques: evidence from analysis of electronic medical records data in Rwanda.BMC medical informatics and decision making · 2025Article
- An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers.PLOS digital health · 2025Article
- Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling.BMC pregnancy and childbirth · 2024Article
- Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life.Scientific reports · 2024Article
- Recent Advances and Challenges in the Early Diagnosis and Treatment of Preterm Labor.Bioengineering (Basel, Switzerland) · 2024Review
- Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms.Frontiers in big data · 2024Article
- Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes.Scientific reports · 2023Article
- Adoption of a National Prophylactic Anticoagulation Guideline for Hospitalized Pregnant Women With COVID-19: Retrospective Cohort Study.JMIR public health and surveillance · 2023Article
- Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women.PloS one · 2023Article
- Prediction and prevention of preterm birth in pregnant women living with HIV on antiretroviral therapy.Expert review of anti-infective therapy · 2022Review
- On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review.Frontiers in public health · 2022Review
- Machine Learning-Based Prediction Model of Preterm Birth Using Electronic Health Record.Journal of healthcare engineering · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preterm birth (PTB) in a pregnant woman is the most serious issue in the field of Gynaecology and Obstetrics, especially in rural India. In recent years, various clinical prediction models for PTB have been developed to improve the accuracy of learning models. However, to the best of the authors' knowledge, most of them suffer from selecting the most accurate features from the medical dataset in linear time. The present paper attempts to design a machine learning model named as risk prediction conceptual model (RPCM) for the prediction of PTB. In this paper, a feature selection approach is proposed based on the notion of entropy. The novel approach is used to find the best maternal features (responsible for PTB) from the obstetrical dataset and aims to predict the classifier's accuracy at the highest level. The paper first deals with the review of PTB cases (which is neglected in many developing countries including India). Next, we collect obstetrical data from the Community Health Centre of rural areas (Kamdara, Jharkhand). The suggested approach is then applied on collected data to identify the excellent maternal features (text-based symptoms) present in pregnant women in order to classify all birth cases into term birth and PTB. The machine learning part of the model is implemented using three different classifiers, namely, decision tree (DT), logistic regression (LR), and support vector machine (SVM) for PTB prediction. The performance of the classifiers is measured in terms of accuracy, specificity, and sensitivity. Finally, the SVM classifier generates an accuracy of
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