Integrating Environmental Pollutant Analytics and Cancer Detection into an AI-Driven Rural Health Cloud with Zero-Trust Security and LDDR Optimization
DOI:
https://doi.org/10.15662/IJEETR.2025.0706007Keywords:
AI-driven rural health cloud, environmental pollutant analytics, cancer detection, zero-trust security, LDDR optimization, IoT-enabled healthcare, machine learning, precision medicine, cloud governance, sustainable healthcare systems.Abstract
The intersection of environmental science and healthcare analytics offers a transformative approach to early disease detection and community health management, particularly in underserved rural regions. This research proposes an AI-driven rural health cloud framework that integrates environmental pollutant analytics and cancer detection systems within a zero-trust security architecture optimized for low data duplication and redundancy (LDDR). The framework employs machine learning and deep neural networks to correlate pollutant exposure data—collected from IoT-enabled environmental sensors—with patient health records to enhance predictive cancer diagnostics. A cloud-native architecture is developed to ensure scalability, interoperability, and secure multi-tenant data processing under stringent privacy and compliance policies. Through zero-trust principles and continuous authentication, the system mitigates insider threats and unauthorized access risks while maintaining high data integrity. LDDR optimization techniques reduce storage overhead and improve computational efficiency across distributed health nodes. Experimental validation demonstrates enhanced accuracy in pollution–disease correlation and significant reductions in operational costs. This study contributes to the development of a sustainable, secure, and intelligent rural healthcare ecosystem, bridging environmental monitoring and precision medicine through AI-driven governance and cloud innovation.
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