EXPLORING THE COMPLEX RELATIONSHIP BETWEEN ASTHMA AND AIR POLLUTION IN URBAN REGIONS: AN ACCURATE AND RELIABLE PREDICTIVE MODEL

Abstract

Asthma is a chronic respiratory disease that affects millions of people worldwide. It is well-established that environmental factors, such as air pollution, can exacerbate asthma symptoms and lead to increased hospitalizations and mortality rates. Understanding the association between asthma and air pollution is crucial for public health interventions and policymaking. Traditionally, epidemiological studies have been conducted to establish the association between asthma and air pollution. These studies involve collecting data from asthma patients, monitoring air quality parameters, and then statistically analyzing the data to identify correlations. While informative, these studies often have limitations such as extensive time requirements, data collection challenges, and the inability to capture real-time associations. Recent days, the machine learning algorithms are gaining lots of attention in many fields including pollution monitoring. Thus, the supervised learning algorithms can gain valuable insights into the complex relationship between asthma and air pollution in urban regions, leading to more targeted and effective interventions for public health. Therefore, this research goal is to develop an accurate and reliable predictive model that can inform public health strategies and policies. This model should aid in proactive decision-making, allowing healthcare providers to allocate resources efficiently and enabling policymakers to implement targeted interventions to reduce air pollution and minimize the impact of asthma on vulnerable urban populations.