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

TL;DR

IyaCare is an integrated AI-IoT-Blockchain platform designed to improve maternal health in resource-limited settings by providing risk assessment, vital monitoring, and secure health records, demonstrating promising accuracy and data integrity.

Contribution

This work introduces a novel, integrated digital health platform combining AI, IoT, and blockchain specifically tailored for maternal health in low-resource environments.

Findings

85.2% accuracy in high-risk pregnancy prediction

Blockchain data integrity validated

Offline-first functionality supports resource-constrained settings

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.