IoT-Enabled Asthma Risk Prediction: Advancements and Challenges

Asthma affects an estimated 262 million people globally, with air pollution identified by the World Health Organization (WHO) as a major trigger for asthma attacks. Despite this, current solutions for monitoring air quality are often costly and not widely accessible, leaving individuals vulnerable and dependent on personal risk assessments. The integration of Internet of Things (IoT) technology offers a promising alternative by enabling real-time monitoring of environmental conditions through devices equipped with microcontrollers and sensors. These devices capture and report air quality data, offering users timely insights to evaluate their surroundings. Furthermore, the application of machine learning (ML) algorithms to the data collected can enable predictive analysis, estimating the probability of an asthma attack based on current and historical air quality readings. This paper examines recent advancements in IoT-based air quality monitoring systems and predictive models for asthma risk assessment. By reviewing current methodologies and innovations in this domain, the paper seeks to clarify how IoT and ML technologies can be applied to support asthma management, bridging gaps in accessibility and empowering individuals with actionable health information. Ultimately, this research aims to demonstrate the potential for IoT to enhance the quality of life for asthma patients worldwide through affordable, data-driven environmental monitoring.