Early Asthma Detection Using Structured Data: Comparative Evaluation of Machine Learning Models

Abstract

Asthma, a prevalent and complex chronic respiratory disease, imposes a growing burden on global healthcare systems. This survey investigates the evolving role of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), in enhancing early detection, personalized care, and real-time monitoring of asthma. Drawing on a structured synthetic dataset encompassing demographic, environmental, lifestyle, and clinical variables, the study applies rigorous data preprocessing and comparative model evaluation across multiple ML algorithms, including Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, and XGBoost. Among these, the XGBoost classifier outperforms others, achieving 98% accuracy and an AUC of 0.94, demonstrating its robustness for structured health data. Additionally, the integration of wearable and real-time sensor data is identified as a critical future direction to further improve predictive performance and clinical applicability. This review highlights the potential of AI-driven approaches to revolutionize asthma care by enabling timely interventions, reducing hospitalizations, and supporting individualized management strategies.