AI-Driven Air Quality and Health Management System through IHIP Platform
Dr. Snehal Golait, Ved Barapatre, Himanshu Dhewle, Tushar Nibrad, Bhupen Patle
DOI : 10.46335/IJIES.2026.11.3.1
Abstract – Air pollution has emerged as a significant environmental risk factor, directly influencing the prevalence of acute respiratory infections and chronic obstructive pulmonary diseases (COPD). While governments utilize platforms like the Integrated Health Information Platform (IHIP) for disease surveillance, there remains a technological disconnect between environmental monitoring systems and public health databases. This paper presents a comprehensive review of an AI-driven approach to bridge this gap. By reviewing recent literature on Machine Learning (ML) and Internet of Things (IoT), propose a software-based architecture that integrates real-time Air Quality Index (AQI) forecasting with health management protocols. The proposed system leverages Deep Learning algorithms, specifically Long Short-Term Memory (LSTM) networks, to predict pollution spikes and trigger automated health advisories within the IHIP ecosystem. This review validates the feasibility of such a system to transition public health policy from reactive treatment to proactive management.
