{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T00:37:00Z","timestamp":1792802220954,"version":"4.5.0"},"reference-count":55,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T00:00:00Z","timestamp":1667952000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Hafr Albatin, Saudi Arabia","id":[{"id":"https:\/\/ror.org\/021jt1927","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Maternal health is an important aspect of women\u2019s 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.<\/jats:p>","DOI":"10.1371\/journal.pone.0276525","type":"journal-article","created":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T13:39:49Z","timestamp":1668001189000},"page":"e0276525","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":112,"title":["Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction"],"prefix":"10.1371","volume":"17","author":[{"given":"Ali","family":"Raza","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0671-2060","authenticated-orcid":true,"given":"Hafeez Ur Rehman","family":"Siddiqui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5114-4213","authenticated-orcid":true,"given":"Kashif","family":"Munir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mubarak","family":"Almutairi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Furqan","family":"Rustam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-6496","authenticated-orcid":true,"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"issue":"4","key":"pone.0276525.ref001","doi-asserted-by":"crossref","first-page":"e0231415","DOI":"10.1371\/journal.pone.0231415","article-title":"What matters to women in the postnatal period: a meta-synthesis of qualitative studies","volume":"15","author":"K Finlayson","year":"2020","journal-title":"PloS one"},{"issue":"1","key":"pone.0276525.ref002","doi-asserted-by":"crossref","first-page":"e0261039","DOI":"10.1371\/journal.pone.0261039","article-title":"Access to gender-affirming hormones during adolescence and mental health outcomes among transgender adults","volume":"17","author":"JL Turban","year":"2022","journal-title":"PLos one"},{"issue":"5","key":"pone.0276525.ref003","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.wombi.2020.09.008","article-title":"Impact of the Helping Mothers Survive Bleeding After Birth learning programme on care provider skills and maternal health outcomes in low-income countries\u00e2\u20ac\u201dAn integrative review","volume":"34","author":"M Bogren","year":"2021","journal-title":"Women and Birth"},{"issue":"4","key":"pone.0276525.ref004","doi-asserted-by":"crossref","first-page":"e0249538","DOI":"10.1371\/journal.pone.0249538","article-title":"Association between postpartum depression level, social support level and breastfeeding attitude and breastfeeding self-efficacy in early postpartum women","volume":"16","author":"Y Mercan","year":"2021","journal-title":"PloS one"},{"key":"pone.0276525.ref005","unstructured":"WHO U. Maternal mortality ratio (modeled estimate, per 100,000 live births) |Data; 2019. Available from: https:\/\/data.worldbank.org\/indicator\/SH.STA.MMRT?end=2017&start=2000&view=chart."},{"key":"pone.0276525.ref006","unstructured":"Max Roser and Hannah Ritchie. Maternal Mortality\u2014Our World in Data;. Available from: https:\/\/ourworldindata.org\/maternal-mortality."},{"key":"pone.0276525.ref007","volume-title":"Global Women\u2019s Health","author":"R Mehboob","year":"2021"},{"key":"pone.0276525.ref008","doi-asserted-by":"crossref","first-page":"228049","DOI":"10.1109\/ACCESS.2020.3042273","article-title":"Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives","volume":"8","author":"S Kaur","year":"2020","journal-title":"IEEE Access"},{"issue":"4","key":"pone.0276525.ref009","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1038\/s42256-021-00309-y","article-title":"A versatile deep learning architecture for classification and label-free prediction of hyperspectral images","volume":"3","author":"B Manifold","year":"2021","journal-title":"Nature machine intelligence"},{"issue":"1","key":"pone.0276525.ref010","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1109\/TCBB.2019.2897679","article-title":"A Deep Learning Framework for Identifying Essential Proteins by Integrating Multiple Types of Biological Information","volume":"18","author":"M Zeng","year":"2021","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"issue":"2","key":"pone.0276525.ref011","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1093\/bib\/bbab087","article-title":"Deep learning in bioinformatics and biomedicine","volume":"22","author":"D Berrar","year":"2021","journal-title":"Briefings in Bioinformatics"},{"issue":"1","key":"pone.0276525.ref012","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1109\/JBHI.2021.3092969","article-title":"Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining\/Deep Learning Architecture","volume":"26","author":"J Theis","year":"2022","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"11","key":"pone.0276525.ref013","doi-asserted-by":"crossref","first-page":"e0242535","DOI":"10.1371\/journal.pone.0242535","article-title":"An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images","volume":"15","author":"D Wang","year":"2020","journal-title":"PloS one"},{"issue":"2","key":"pone.0276525.ref014","doi-asserted-by":"crossref","first-page":"e1007025","DOI":"10.1371\/journal.pcbi.1007025","article-title":"Deep Learning-driven research for drug discovery: Tackling Malaria","volume":"16","author":"BJ Neves","year":"2020","journal-title":"PLoS computational biology"},{"issue":"4","key":"pone.0276525.ref015","doi-asserted-by":"crossref","first-page":"e0232391","DOI":"10.1371\/journal.pone.0232391","article-title":"Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study","volume":"15","author":"GS Randhawa","year":"2020","journal-title":"Plos one"},{"key":"pone.0276525.ref016","doi-asserted-by":"crossref","unstructured":"Krisnanik E, Tambunan K, Irmanda HN. Analysis of Pregnancy Risk Factors for Pregnant Women Using Analysis Data Based on Expert System. Proceedings\u20141st International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2019. 2019; p. 151\u2013156.","DOI":"10.1109\/ICIMCIS48181.2019.8985211"},{"key":"pone.0276525.ref017","doi-asserted-by":"crossref","unstructured":"Ahmed M, Kashem MA. IoT Based Risk Level Prediction Model for Maternal Health Care in the Context of Bangladesh. 2020 2nd International Conference on Sustainable Technologies for Industry 40, STI 2020. 2020;0:19\u201320.","DOI":"10.1109\/STI50764.2020.9350320"},{"key":"pone.0276525.ref018","doi-asserted-by":"crossref","unstructured":"Pirjatullah, Kartini D, Nugrahadi DT, Muliadi, Farmadi A. Hyperparameter Tuning using GridsearchCV on The Comparison of The Activation Function of The ELM Method to The Classification of Pneumonia in Toddlers. In: 2021 4th International Conference of Computer and Informatics Engineering (IC2IE); 2021. p. 390\u2013395.","DOI":"10.1109\/IC2IE53219.2021.9649207"},{"key":"pone.0276525.ref019","doi-asserted-by":"crossref","unstructured":"Lakshmi BN, Indumathi TS, Ravi N. A novel health monitoring approach for pregnant women. 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2015. 2016;48:324\u2013328.","DOI":"10.1109\/ERECT.2015.7499035"},{"key":"pone.0276525.ref020","unstructured":"N LB, S DIT, Ravi DN. A Comparative Study of Classification Algorithms for Risk Prediction in Pregnancy. TENCON 2015\u20142015 IEEE Region 10 Conference. 2015;."},{"key":"pone.0276525.ref021","doi-asserted-by":"crossref","unstructured":"Indumathi N, Ramalakshmi R, Ajith V. Analysis of risk factors in the Firework Industries: Using Decision Tree Classifier. In: 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE); 2021. p. 811\u2013814.","DOI":"10.1109\/ICACITE51222.2021.9404726"},{"key":"pone.0276525.ref022","first-page":"3","author":"H Jurzwk","journal-title":"Building Prediction Models for Maternal Mortality"},{"key":"pone.0276525.ref023","doi-asserted-by":"crossref","unstructured":"Ramla M, Sangeetha S, Nickolas S. Fetal Health State Monitoring Using Decision Tree Classifier from Cardiotocography Measurements. Proceedings of the 2nd International Conference on Intelligent Computing and Control Systems, ICICCS 2018. 2019;(Iciccs):1799\u20131803.","DOI":"10.1109\/ICCONS.2018.8663047"},{"key":"pone.0276525.ref024","first-page":"397","article-title":"Machine learning analysis for remote prenatal care","author":"JM Bautista","year":"2020","journal-title":"IEEE Region 10 Annual International Conference, Proceedings\/TENCON"},{"key":"pone.0276525.ref025","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2019.2955458","article-title":"Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT","volume":"8","author":"A Masood","year":"2020","journal-title":"IEEE Journal of Translational Engineering in Health and Medicine"},{"issue":"23","key":"pone.0276525.ref026","doi-asserted-by":"crossref","first-page":"16814","DOI":"10.1109\/JIOT.2020.3037759","article-title":"IoT-Based Smart Health System for Ambulatory Maternal and Fetal Monitoring","volume":"8","author":"JAL Marques","year":"2021","journal-title":"IEEE Internet of Things Journal"},{"issue":"2","key":"pone.0276525.ref027","doi-asserted-by":"crossref","DOI":"10.5958\/0976-5506.2019.00298.5","article-title":"Efficient Approaches for Prediction of Brain Tumor using Machine Learning Techniques","volume":"10","author":"JA Alzubi","year":"2019","journal-title":"Indian Journal of Public Health Research & Development"},{"issue":"2","key":"pone.0276525.ref028","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.3233\/JIFS-189786","article-title":"Distracted driver detection using compressed energy efficient convolutional neural network","volume":"42","author":"JA Alzubi","year":"2022","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"issue":"4","key":"pone.0276525.ref029","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1109\/TCYB.2018.2877663","article-title":"Geometric Structural Ensemble Learning for Imbalanced Problems","volume":"50","author":"Z Zhu","year":"2020","journal-title":"IEEE Transactions on Cybernetics"},{"key":"pone.0276525.ref030","doi-asserted-by":"crossref","unstructured":"Bin Alam MS, Patwary MJA, Hassan M. Birth Mode Prediction Using Bagging Ensemble Classifier: A Case Study of Bangladesh. 2021 International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2021\u2014Proceedings. 2021; p. 95\u201399.","DOI":"10.1109\/ICICT4SD50815.2021.9396909"},{"key":"pone.0276525.ref031","doi-asserted-by":"crossref","unstructured":"Pereira SSL, Valter Costa Filho R, Ramos R, Oliveira M, Moreira MWL, Rodrigues JJPC, et al. Improving Maternal Risk Analysis in Public Health Systems. 2020 5th International Conference on Smart and Sustainable Technologies, SpliTech 2020. 2020.","DOI":"10.23919\/SpliTech49282.2020.9243769"},{"key":"pone.0276525.ref032","unstructured":"UCI Machine Learning Repository: Maternal Health Risk Data Set Data Set \u2014 archive.ics.uci.edu;. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Maternal+Health+Risk+Data+Set."},{"key":"pone.0276525.ref033","doi-asserted-by":"crossref","first-page":"38236","DOI":"10.1109\/ACCESS.2021.3056672","article-title":"An IoT-Based Real-Time Intelligent Monitoring and Notification System of Cold Storage","volume":"9","author":"H Afreen","year":"2021","journal-title":"IEEE Access"},{"key":"pone.0276525.ref034","unstructured":"CSAFRIT. Maternal Health Risk Data | Kaggle; 2022. Available from: https:\/\/www.kaggle.com\/datasets\/csafrit2\/maternal-health-risk-data."},{"issue":"9","key":"pone.0276525.ref035","doi-asserted-by":"crossref","first-page":"5867","DOI":"10.1109\/TII.2020.3046566","article-title":"Imbalanced Classification Based on Minority Clustering Synthetic Minority Oversampling Technique With Wind Turbine Fault Detection Application","volume":"17","author":"H Yi","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"pone.0276525.ref036","doi-asserted-by":"crossref","first-page":"134209","DOI":"10.1109\/ACCESS.2020.3010815","article-title":"Soft-Ranking Label Encoding for Robust Facial Age Estimation","volume":"8","author":"X Zeng","year":"2020","journal-title":"IEEE Access"},{"issue":"4","key":"pone.0276525.ref037","doi-asserted-by":"crossref","first-page":"4811","DOI":"10.1109\/JSEN.2020.3028738","article-title":"Time Series Classification-Based Correlational Neural Network With Bidirectional LSTM for Automated Detection of Kidney Disease","volume":"21","author":"N Bhaskar","year":"2021","journal-title":"IEEE Sensors Journal"},{"key":"pone.0276525.ref038","article-title":"A novel improved random forest for text classification using feature ranking and optimal number of trees","author":"N Jalal","year":"2022","journal-title":"Journal of King Saud University-Computer and Information Sciences"},{"key":"pone.0276525.ref039","doi-asserted-by":"crossref","first-page":"95456","DOI":"10.1109\/ACCESS.2021.3094532","article-title":"Adaptable reduced-complexity approach based on state vector machine for identification of criminal activists on social media","volume":"9","author":"I Shafi","year":"2021","journal-title":"IEEE Access"},{"issue":"5","key":"pone.0276525.ref040","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1109\/TLA.2021.9448317","article-title":"Convolutional Neural Networks and Long Short-Term Memory Networks for Textual Classification of Information Access Requests","volume":"19","author":"E Paiva","year":"2021","journal-title":"IEEE Latin America Transactions"},{"issue":"13","key":"pone.0276525.ref041","doi-asserted-by":"crossref","first-page":"10953","DOI":"10.1109\/JIOT.2021.3051247","article-title":"Passive RFID Module With LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient-Assisted Living","volume":"8","author":"GA Oguntala","year":"2021","journal-title":"IEEE Internet of Things Journal"},{"key":"pone.0276525.ref042","doi-asserted-by":"crossref","first-page":"3228","DOI":"10.1109\/ACCESS.2022.3140342","article-title":"A Deep Bidirectional LSTM-GRU Network Model for Automated Ciphertext Classification","volume":"10","author":"E Ahmadzadeh","year":"2022","journal-title":"IEEE Access"},{"key":"pone.0276525.ref043","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2021.3095505","article-title":"Channel Attention-Based Temporal Convolutional Network for Satellite Image Time Series Classification","volume":"19","author":"P Tang","year":"2022","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"pone.0276525.ref044","first-page":"1925","article-title":"Adaptive Distance-Based Pooling in Convolutional Neural Networks for Audio Event Classification","volume":"28","author":"I Mart\u00c3\u00adn-Morat\u00c3\u00b3","year":"2020","journal-title":"IEEE\/ACM Transactions on Audio, Speech, and Language Processing"},{"key":"pone.0276525.ref045","doi-asserted-by":"crossref","first-page":"4493","DOI":"10.1109\/JSTARS.2020.3014966","article-title":"A New Parallel Dual-Channel Fully Convolutional Network Via Semi-Supervised FCM for PolSAR Image Classification","volume":"13","author":"F Zhao","year":"2020","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"11","key":"pone.0276525.ref046","doi-asserted-by":"crossref","first-page":"3331","DOI":"10.1109\/TMI.2020.2990625","article-title":"Layer Embedding Analysis in Convolutional Neural Networks for Improved Probability Calibration and Classification","volume":"39","author":"F Zhang","year":"2020","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"pone.0276525.ref047","first-page":"59","article-title":"Architectural Implications of Graph Neural Networks","volume":"19","author":"Z Zhang","year":"2020","journal-title":"IEEE Computer Architecture Letters"},{"key":"pone.0276525.ref048","doi-asserted-by":"crossref","first-page":"52588","DOI":"10.1109\/ACCESS.2020.2981072","article-title":"Basic Enhancement Strategies When Using Bayesian Optimization for Hyperparameter Tuning of Deep Neural Networks","volume":"8","author":"H Cho","year":"2020","journal-title":"IEEE Access"},{"key":"pone.0276525.ref049","doi-asserted-by":"crossref","first-page":"148793","DOI":"10.1109\/ACCESS.2020.3016255","article-title":"An Internet of Things Based Smart Waste Management System Using LoRa and Tensorflow Deep Learning Model","volume":"8","author":"TJ Sheng","year":"2020","journal-title":"IEEE Access"},{"issue":"7","key":"pone.0276525.ref050","doi-asserted-by":"crossref","first-page":"2995","DOI":"10.1109\/TKDE.2019.2959988","article-title":"Tackling Overfitting in Boosting for Noisy Healthcare Data","volume":"33","author":"Y Park","year":"2021","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"pone.0276525.ref051","doi-asserted-by":"crossref","unstructured":"Ahmed H, Traore I, Saad S. Detection of online fake news using n-gram analysis and machine learning techniques. In: International conference on intelligent, secure, and dependable systems in distributed and cloud environments. Springer; 2017. p. 127\u2013138.","DOI":"10.1007\/978-3-319-69155-8_9"},{"key":"pone.0276525.ref052","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/978-981-15-2317-5_30","volume-title":"InECCE2019","author":"M Ahmed","year":"2020"},{"key":"pone.0276525.ref053","first-page":"1","article-title":"Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients","author":"P Subramani","year":"2021","journal-title":"Personal and ubiquitous computing"},{"issue":"2","key":"pone.0276525.ref054","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s11334-021-00388-5","article-title":"On the use of textual feature extraction techniques to support the automated detection of refactoring documentation","volume":"18","author":"L Marmolejos","year":"2022","journal-title":"Innovations in Systems and Software Engineering"},{"issue":"2","key":"pone.0276525.ref055","doi-asserted-by":"crossref","first-page":"e0245909","DOI":"10.1371\/journal.pone.0245909","article-title":"A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis","volume":"16","author":"F Rustam","year":"2021","journal-title":"Plos one"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0276525","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T16:48:27Z","timestamp":1728319707000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0276525"}},"subtitle":[],"editor":[{"given":"Mohamed","family":"Hammad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2022,11,9]]},"references-count":55,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11,9]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0276525","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,9]]}}}