Early Asthma Detection Using Structured Data: Comparative Evaluation of Machine Learning Models | ||||
| International Journal of Telecommunications | ||||
| Volume 05, Issue 02, July 2025, Page 1-22 PDF (2.09 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijt.2025.395977.1119 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
ghada Abd El-Latif shatat  1; Hossam el-din Moustafa 2; Mohamed Saraya3; Al-Saeed Ahmed Marzouk2; ASMAA AEL HEKAL4
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| 1Mansoura | ||||
| 2Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University | ||||
| 3Computer and control systems engineering, faculty of engineering, Mansoura university, | ||||
| 4Electrical Department, Faculty of Engineering, Modern University for Technology and Information (MTI University), Cairo, 11439, Egypt | ||||
| 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. | ||||
| Keywords | ||||
| Artificial Intelligence; XGBoost; Predictive Modeling; Wearable Devices; Personalized Healthcare | ||||
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