A comprehensive maternal health risk prediction dataset from IoT-enabled medical cyber-physical systems in developing countries: Supporting deep learning applications for clinical decision support

A comprehensive maternal health risk prediction dataset from IoT-enabled medical cyber-physical systems in developing countries: Supporting deep learning applications for clinical decision support | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A comprehensive maternal health risk prediction dataset from IoT-enabled medical cyber-physical systems in developing countries: Supporting deep learning applications for clinical decision support Mohammad Mobarak Hossain, Nasim Mahmud Nayan, Mohammod Abdul Kashem This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7405384/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Feb, 2026 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted 12 You are reading this latest preprint version Abstract Background : This data article presents a comprehensive dataset of 6,103 maternal health records collected through Internet of Things (IoT)-enabled Medical Cyber-Physical Systems (MCPS) across multiple healthcare facilities in Bangladesh. Methods : The dataset comprises 8 attributes including clinical measurements (age, body temperature, heart rate, systolic and diastolic blood pressure, BMI, blood glucose levels) and risk classifications (high, mid, low risk). Data was collected using standardized IoT sensors including Raspberry Pi 4 controllers integrated with medical-grade sensors and validated by medical experts from 9 healthcare institutions between February 2021 and January 2023. Results : The dataset demonstrates balanced class distribution (Figure 1) and achieved up to 94.51% prediction accuracy using a Simple Recurrent Neural Network (RNN) with comprehensive cross-validation (Table 3). Temporal sequence modeling demonstrates strong performance, enabling enhanced interpretability through the use of attention mechanisms (Figure 9). Conclusions : This dataset addresses the critical need for maternal health risk prediction in resource-limited settings where traditional healthcare access is challenging. The data support the development of deep learning models, the creation of clinical decision support systems, and research on maternal mortality reduction. All data underwent rigorous quality control, expert validation, and ethical approval processes, making it suitable for academic research, healthcare technology development, and public health policy formulation in developing countries. Maternal health IoT sensors Deep learning RNN LSTM Risk prediction Medical cyber-physical systems Healthcare analytics Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 12 Feb, 2026 Read the published version in BMC Medical Informatics and Decision Making → Version 1 posted Editorial decision: Revision requested 08 Oct, 2025 Reviews received at journal 04 Oct, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviews received at journal 11 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers invited by journal 10 Sep, 2025 Editor invited by journal 08 Sep, 2025 Editor assigned by journal 05 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 03 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7405384","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514691875,"identity":"ba5696ba-4f87-44f6-a1a8-766ba34a2da3","order_by":0,"name":"Mohammad Mobarak 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