IyaCare: An Integrated AI-IoT-Blockchain Platform for Maternal Health in Resource-Constrained Settings

TL;DR

IyáCare addresses the high maternal mortality in Sub-Saharan Africa by proposing an integrated AI-IoT-Blockchain platform tailored for resource-constrained settings. The authors present a six-layer architecture with offline-first design, validated by an 85.2% AI risk-prediction model and 99.8% blockchain data integrity in a proof-of-concept. The work demonstrates feasible interoperability between predictive analytics, continuous monitoring, and secure health records, with concrete workflows and latency targets for clinical use. Limitations include synthetic validation and simulated deployment, but the study provides a replicable architectural model and clear directions for real-world trials and regional data integration.

Abstract

Maternal mortality in Sub-Saharan Africa remains critically high, accounting for 70% of global deaths despite representing only 17% of the world population. Current digital health interventions typically deploy artificial intelligence (AI), Internet of Things (IoT), and blockchain technologies in isolation, missing synergistic opportunities for transformative healthcare delivery. This paper presents IyaCare, a proof-of-concept integrated platform that combines predictive risk assessment, continuous vital sign monitoring, and secure health records management specifically designed for resource-constrained settings. We developed a web-based system with Next.js frontend, Firebase backend, Ethereum blockchain architecture, and XGBoost AI models trained on maternal health datasets. Our feasibility study demonstrates 85.2% accuracy in high-risk pregnancy prediction and validates blockchain data integrity, with key innovations including offline-first functionality and SMS-based communication for community health workers. While limitations include reliance on synthetic validation data and simulated healthcare environments, results confirm the technical feasibility and potential impact of converged digital health solutions. This work contributes a replicable architectural model for integrated maternal health platforms in low-resource settings, advancing progress toward SDG 3.1 targets.

Figures (2)

• Figure 1: IyáCare system architecture showing the six-layer integrated platform
• Figure 2: Confusion Matrix for the XGBoost Risk Prediction Model