Enhancing Airborne Disease Prediction: Integrating Deep Infomax and Self-Organizing Maps for Risk Factor Identification

Abstract

Asthma poses a significant global public health concern, particularly in urban centers where environmental pollutants and variable weather patterns contribute to heightened prevalence and symptom exacerbation. The Deonar dumping ground, one of Mumbai’s largest landfills, releases a complex mix of particulate matter and hazardous gases, posing a serious threat to local respiratory health. Despite the urgency for comprehensive research integrating patient-specific data with localized weather and air quality metrics, such studies remain limited. This study addresses the critical research gap by investigating asthma risk factors near the Deonar dumping ground. Integrating detailed patient records with precise local weather and air quality measurements, our research aims to unravel the intricate relationship between environmental exposure and respiratory health outcomes. The findings provide crucial insights into the specific risk factors influencing asthma incidence and severity in this region, informing the development of targeted interventions and mitigation strategies. Employing a novel ensemble Deep Info Max - Self-Organizing Map (DIM-SOM) technique, our study compares its performance with various clustering algorithms, including SOM, K-Means, Bisecting K-Means, DBSCAN, and others. The novel ensemble DIM-SOM demonstrated superior performance, achieving a significantly higher Silhouette Score of 0.9234, a lower Davies-Bouldin Score of 0.1276, and a more favorable Calinski-Harabasz Score of 389723.6225 compared to other algorithms. These findings underscore the efficacy of the novel ensemble DIM-SOM approach in generating dense, wellseparated, and meaningful clusters, emphasizing its potential to enhance clustering performance compared to traditional algorithms. The study further emphasizes the need for proactive mitigation measures and tailored healthcare interventions based on the identified environmental risk factors.